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IBM’s artificial intelligence (AI) portfolio carries a few of the market’s most widely used AI solutions.

With just over 340,000 employees, Armonk, New York-based IBM is able to provide technical services such as AI to companies in over 171 countries.

IBM reported $303.8 million in AI revenue for 2023, according to a 2023 report by IDC. 

See below to learn about IBM’s artificial intelligence offerings, which are a key part the company’s global portfolio:

Watson Explorer is a cognitive exploration solution. Its combination of search and content analytics aims to help users find and understand the results of their inquiries.

Understands the structures of documents

Mines texts for insights

Delivers smart passages for answers

Chatbot integration

Watson Machine Learning is a full-service cloud offering. It allows data scientists and developers to create applications with predictive capabilities integrated. 

Machine learning (ML) model training and development

model training and development

Advanced data


Visual tooling integration

Decision optimization capabilities

OpenPages is IBM’s governance, risk, and compliance (GRC) platform. It empowers companies to manage risks and regulatory compliance across their organization and spanning multiple regulatory requirements.

Single data repository

Dynamic and user-friendly dashboard

Embedded flexible workflow models

A task-focused user interface

Watson Knowledge Catalog is a centralized cloud-based data cataloging tool. It’s an intelligent self-serving repository that provides data for AI, machine learning, and deep learning applications.

End-to-end data catalog

Automated governance

Flexible deployment in cloud


as a managed service

Ability to share self-service insights with peers and analytics tools

Watson Natural Language Understanding is a natural language processing (NLP) service and text analysis tool. Deep learning algorithms are used to extract meaning and metadata from unstructured blocks of text to provide insights.

Customizable to understand the precise language of a company 

Real-time text analysis and actionable insights

Flexibility to deploy in cloud or behind a local firewall

Creates employee-centered strategies

Mitigates risk of harm exposure

Creates a flexible workplace that prioritizes employee safety

Monitors and enhances customer service

Conducts regular social listening on online conversations

Built-in chatbot integration

Watson Captioning Live uses a combination of ML, AI, and cognitive speech-to-text applications to deliver automated real-time captions for audio files.

Saves time compared to manual captioning

Trained to understand various accents and local idioms

Creates more accessible media

Watson Assistant is a white-label cloud service that enables software developers to embed AI and virtual assistant (VA) functionality into their company’s software and offer it to clients.

Easy to deploy with a drag-and-drop interface

Integrates with multiple channels and existing content

Enterprise-ready and scalable

Watson Studio is IBM’s software platform for developers, data scientists, and analysts to develop, run, and manage AI models.

AutoAI for building model pipelines

Ability to manipulate, cleanse, and structure data on-site

Model risk evaluation and management

Built-in metrics monitoring and insights

Integrated data visualization tools

See more: Artificial Intelligence: Current and Future Trends

IBM offers the companies the chance to become a Watson AI partner to get hands-on support from their AI experts.

Partners are also able to join IBM’s live AI workshops and webinars at select locations.

IBM’s AI solutions have been used by thousands of clients in a variety of industries from all around the globe.

GM Financial, the financial arm of General Motors, serves millions of customers in the automotive industry. 

One issue they faced was the lack of engagement with their previous customer support chatbot.

“So, our first chatbot was just an FAQ bot. It didn’t leverage artificial intelligence by any means. And it was very unsatisfactory,” says Bob Beatty, EVP of customer experience at GM Financial.

They utilized Watson Assistant to answer customer questions and improve employee workday.

“We’re looking at Watson as another agent,” Beatty says. “And we’re telling our team members you have a new team member, and it’s IBM Watson Assistant.”

Overall, IBM AI Watson solutions have positive reviews regarding efficiency and quality.

A few downsides that some corporate users describe are the lack of compatibility with some systems and apps, slow loading time, and a learning curve.

TrustRadius: 8.8 out of 10

Gartner Peer Insights: 4.4 out of 5

Capterra: 4.3 out of 5

IBM has a track record of releasing acclaimed AI products.

Watson won the prestigious Gottlieb Duttweiler prize in 2023 for its outstanding contribution to the well-being of the economic environment.

Additionally, IBM Watson OpenPages recently won the Red Dot design award for the best in design.

IBM holds the largest share of the AI software market (8.8% in 2023), according to a 2023 report by IDC.

Microsoft holds the second largest share of the AI software market in the report (5.6%), and SAS ranks third (4.4%).

The AI software market was worth an estimated $3.5 billion in 2023, IDC says.

It is the fifth consecutive year IBM ranks as the market share leader in the report.

See more: Top Performing Artificial Intelligence Companies

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Examples Of Artificial Intelligence (Ai) In 7 Industries

blog / Artificial Intelligence and Machine Learning Examples of Artificial Intelligence (AI) in 7 Industries

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Artificial intelligence (AI) is a hot topic. But for many, the application of AI beyond Apple’s Siri and Amazon’s Alexa remains murky. 

New and emerging use cases for AI are expected to transform nearly every industry in the coming years and decades. And savvy companies and business leaders stand to benefit.

What Is Artificial Intelligence


Broadly speaking, AI refers to computer systems that can perform problem-solving and decision-making tasks normally associated with human intelligence. These can include:

Recognizing images and speech

Making decisions

Translating languages

Providing recommendations

And more

Examples of Artificial Intelligence Across Industries

So, how is artificial intelligence used? 

AI applications range from consumer-oriented solutions (such as chatbots) to highly complex industrial use cases, like predicting the need for manufacturing equipment maintenance. Examples from seven industries provide a glimpse into the breadth and depth of the possibilities for AI. 

1. Financial Services

AI has numerous applications in both consumer finance and global banking operations. Examples of artificial intelligence in this industry include the following.

Fraud Detection

Financial fraud attempts, whether on a massive scale or through day-to-day crimes (like credit card skimming), continue to rise rapidly in frequency and cause major disruptions to both organizations and individuals. According to Business Insider, banks like J.P. Morgan Chase use proprietary artificial intelligence algorithms to flag transactions that don’t fit normal patterns for further inspection.

Algorithmic Trading

Gone are the days of traders shouting on the stock market floor. Today, most major trading transactions are handled by algorithms that react and make decisions far faster than humans ever could. In fact, by 2024, the algorithmic trading industry is expected to reach $19 billion annually. 

2. Insurance

Within the broader landscape of financial services, insurance stands out for its unique applications of artificial intelligence. These include:

AI-Powered Underwriting

For decades, underwriting decisions have relied on highly manual processes and data inputs, as well as invasive processes like medical exams. Today, insurance companies use AI to assess risks using massive data sets that draw on factors ranging from prescription drug history to pet ownership. 

Claims Processing

Today, artificial intelligence can handle much of the claims process for simple claims. Examples of artificial intelligence range from handling client interactions through chatbots (like Progressive’s Flo) to using machine vision to assess auto damage. As machine vision and AI capabilities increase, human involvement will likely play less of a role in claims decisions. 

3. Healthcare

While healthcare has traditionally relied heavily on human labor and care, an increasing number of tasks can now be outsourced to AI. Below are two examples of AI in healthcare.

Precision Medicine and Algorithms

An individual’s health outcomes, or even their response to a certain treatment, can vary significantly based on numerous factors, from lifestyle to genetics. These factors are difficult for human doctors to parse. AI can take in huge amounts of data to identify optimal treatments for patients, or even to identify emerging health concerns before they rise to a level that a human might notice.

Computer Vision for Diagnosis and Surgery

Increasingly, computer vision and machine learning are showing promise for diagnosing conditions such as skin cancers, and even for assisting during complex surgeries. For instance, AI can ensure physicians perform all necessary steps correctly during an operation. 

4. Life Sciences

Since the life sciences by nature involve large data sets generated by experiments, it’s not surprising that artificial intelligence has numerous potential applications in the field. Artificial intelligence examples in life sciences include:

Drug Discovery

The search for novel treatments still requires large-scale experiments and confirmation of hypotheses. However, machine learning has been used since the 1990s to speed the process considerably. It can predict how certain compounds will interact with each other and even how a drug will work against its target.

Predicting Disease Spread

Throughout the COVID-19 pandemic, experts have used AI and machine learning extensively to predict the spread and impacts of the virus, particularly as it has mutated. Data from these models has allowed public health and healthcare leaders to adopt policies and prepare resources to minimize spikes and reduce stress on the broader healthcare system.

5. Telecommunications

While many of us often take internet and communications availability for granted, the telecommunications industry depends on a series of highly complex processes and constant adjustments. AI can address these needs in several ways.

Network Optimization

To maintain flawless operations, networks need to adapt to changing traffic and quickly address anomalies. Currently, 63.5% of telecommunications providers are using AI to monitor and improve their networks and deliver the best possible performance for their end customers.

Predictive Maintenance

Telecommunications networks rely on broadly distributed sets of hardware. And problems within this infrastructure can ripple throughout the network. Artificial intelligence offers telecommunications companies the opportunity to use predictive algorithms to identify when problems are most likely to arise, allowing 

6. Oil, Gas, Energy

Oil, gas, and energy is an increasingly complex field, and one with little room for error given safety and environmental considerations. Artificial intelligence is allowing energy companies to increase their efficiency without increasing costs. Applications include:

Image-Processing to Identify Maintenance Needs

AI’s increasing ability to process images and recognize patterns is making it possible to use drones and other image sources to check power infrastructures for equipment breakdowns or even downed wires. This is a tactic that has already been implemented across the United Kingdom’s power grid. 

Anticipating Energy Demand

As the transition to renewable energy continues, predictive data on energy demand and availability will be essential for energy providers as they make decisions about storage and utilization.  This can include identifying how much solar power needs to be stored for nighttime or rainy days. AI will help companies parse the factors that impact demand and make informed decisions for the future.

7. Aviation

Safe, efficient aviation, especially in the context of rising fuel prices, depends on the careful use of data to optimize both individual flights and the broader aviation infrastructure. AI applications in this sector include:

Predicting Route Demand

To maximize profits while retaining customer loyalty, airlines must strike a careful balance between providing enough flights between specific destinations without flying more routes than is economical. AI models can take factors like internet traffic, macroeconomic trends, and seasonal tourism data into account to help airlines make informed decisions about their route offerings. 

Providing Customer Service

During major disruptions, such as those caused by massive weather events, few airlines have the staffing capacity to handle individual customer queries and needs one-on-one. In addition to automated messaging, airlines increasingly rely on AI to extract key pieces of information from customer messages and provide an appropriate response. For example, this may involve directing a customer inquiring about their luggage to information about reporting lost baggage. 

The Future of AI Across Industries

As the breadth and depth of AI applications demonstrate, applications of big data, machine learning, and more have major implications across industries. While some of these applications are nascent, they are likely only the tip of the iceberg as this technology matures. Organizations, therefore, should consider acting quickly to scale up their internal capacity to investigate and apply AI. 

By Rachel Hastings

Ready to build artificial intelligence capabilities within your organization? View our selection of online artificial intelligence courses. You can also learn more about a new Emeritus Enterprise course on Designing and Building AI Products and Services for Regulated Industries, available for individual and group enrollment.

Ai Vocabulary: 10 Key Terms Defining Artificial Intelligence

Big data to ChatGPT; here are 10 key terms that will define artificial intelligence

Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition, and machine vision. The adoption of AI has been driven not just by increased computational power and new algorithms yet additionally by the growth of data now accessible. This article will discuss the 10 key terms defining artificial intelligence in 2023.

Big Data

Massive data sets that are statistically analyzed to gain detailed insights. The data can involve billions of records and require substantial computer-processing power. Datasets are sometimes linked together to see how patterns in one domain affect other areas. Data can be structured into fixed fields or unstructured as free-flowing information. The analysis of big data, often using AI, can reveal patterns, trends, or underlying relationships that were not previously apparent to researchers.


A chatbot, or a conversational agent or virtual assistant, is a system capable of conversing with users based on conversations scripted upstream. Its role is to respond with maximum relevance to questions often requested by internet users, clients, or personnel. As a result, recurring tasks can be automated, permitting employees to use their time better.


ChatGPT interface is built on top of GPT-3.5. GPT-3.5 is a significant language model developed by OpenAI that is trained on a massive amount of internet text data and fine-tuned to perform a wide range of natural language tasks. Example: GPT-3.5 has been fine-tuned for tasks such as language translation, text summarization, and question answering.

Cloud Robotics

A field of robotics that attempts to invoke cloud technologies such as cloud computing, cloud storage, and other Internet technologies centered on the benefits of converged infrastructure and shared services for robotics. When connected to the cloud, robots can benefit from the robust computation, storage, and communication resources of modern data centers in the cloud, which can process and share information from various robots or agents (other machines, intelligent objects, humans, etc.). Humans can also delegate tasks to robots remotely through networks.

Deep Learning

Deep learning is another area of artificial intelligence that relies on artificial neural networks. This method encourages computers and other devices to learn by doing, much like people do. Because neural networks have hidden layers, the word “deep” was created. To automate predictive analytics, a hierarchy of algorithms is used. Deep learning has gained traction in various industries, including the aerospace and military, to recognize things from satellites, worker safety by identifying dangerous situations when a worker is near a machine, cancer cell detection, etc.

Edge Computing

Edge computing brings computations closer to data sources, reducing latency, bandwidth, and energy usage. Developers and enterprises can dramatically lower the infrastructure requirements for real-time data processing using AI at the edge. To avoid system failure, intelligent cities, factories, and automobiles for autonomous driving systems companies integrate this technology.

Game AI

Game AI is a type of AI that uses an algorithm to replace randomness in video games. It’s a computational behavior used by non-player characters to generate humanlike intelligence and reactive behaviors taken by the player in tournaments. It is one of the most searched Artificial intelligence terms.


GPT-4 is the latest model addition to OpenAI’s deep learning efforts and is a significant milestone in scaling deep learning. GPT-4 is also the first of the GPT models that is a sizeable multimodal model, meaning it accepts both image and text inputs and emits text outputs.

Large Language Models

LLMs use machine learning algorithms to predict human language, code, and even perform sentiment analysis. LLMs in the future, instead of just regurgitating words, will likely reflect sentiment to the tee. 

Machine Learning

Artificial Intelligence – Benefits, Risks And Myth


Artificial Intelligence (AI) is intelligence process by machines or connect a hardware with programming language. AI is an area of computer science, connect hardware, device with programming language and the machine work will exactly like a human.

AI works by combining a large number of data, fast response, intelligent algorithm, great process and allows the software to learn automatically from data you have and respond according to them.

From SIRI into self-driving automobiles, artificial intelligence (AI) is progressing quickly. While science fiction frequently describes AI as robots using human attributes, AI can encompass anything from Google’s search algorithms to IBM’s Watson to autonomous firearms.

Why Research AI Safety?

In the near term, the goal of keeping AI’s impact on society beneficial has spurred research in many areas ranging from economics and law to technical topics such as verification, legitimacy, security and control. While it can be little more than a minor nuisance if your laptop crashes or gets hacked, it all becomes more important than an AI system does what you want to do if it is your car, your The airplane, your pacemaker, controls your automated trading system or your power grid. Another short-term challenge is the use of destructive weapons in deadly autonomous weapons. Yes, is canceled.

In the long term, an important question is if the discovery of strong AI succeeds and an AI system outperforms humans in all cognitive functions. As I.J. Designing a good, smart AI system in 1965 is a cognitive task in itself. Such a system can potentially undergo recurring self-improvement, triggering an intelligence explosion that leaves human intelligence far behind. By inventing revolutionary new technologies, such a superintelligence can help us eradicate war, disease, and poverty, and so the creation of strong AI may be the greatest event in human history. However, some experts have expressed concern, that it may even be the last one until we learn to align the goals of AI’s first with Super.

How can AI be Dangerous?

Most researchers agree that a superintendent AI is unlikely to exhibit human emotions such as love or hate, and there is no reason to expect AI to be intentionally altruistic or moralistic. Instead, considering AI when posing a risk, experts consider the two scenarios most likely:

AI is programmed to be something beneficial, but it develops a destructive method to achieve its goal: this can happen even when we fail to align the goals of AI with ourselves completely Are, which is very difficult. If you could ask an obedient intelligent car to take you to the airport as soon as possible, you might be chased by helicopters there and covered with vomit, which you didn’t want, but actually you What was asked for. If a superintending system is worked out with an ambitious geoengineering project, it can wreak havoc with our ecosystem as a side effect, and to see human efforts as a threat to stop it Does.

Also read: Top 10 Best Artificial Intelligence Software

Why the recent interest in AI safety

Stephen Hawking, Elon Musk, Steve Wozniak, Bill Gates, and lots of other big names in technology and science have recently expressed concern from social media and through open letters concerning the dangers introduced by AI, combined by several prominent AI researchers. What’s the topic abruptly in the headlines?

The thought that the pursuit of strong AI would finally triumph was thought of as science fiction, centuries or even more away. But as a result of recent discoveries, many AI landmarks, which specialists viewed as years away only five decades back, have been attained, making many specialists take seriously the potential for superintelligence within our life. When some experts still suspect that human-level AI is centuries off, many AI researches in the 2023 Puerto Rico Conference suspected it would occur before 2060. As it might take decades to finish the compulsory safety study, it’s wise to begin it today.

Because AI has the potential to become more intelligent than any human, we have no definitive way of predicting how it will behave. We cannot use past technological developments as a basis because we have never created anything that outsources to us, unknowingly or unknowingly. The best example of what we can face may be our own development. People now control the planet, not because we are the strongest, fastest or largest, but because we are the smartest. If we are not the smartest yet, are we confident of remaining in control?

A fascinating conversation is taking place about the future of artificial intelligence and what it will / should mean for humanity. There are fascinating controversies where the world’s leading experts disagree, such as the impact of AI on the future job market; If / when human-level AI will be developed; Will it lead to an intelligence explosion; And is this something we should welcome or fear. But there are also many examples of boring pseudo-controversies caused by people misunderstanding and talking to each other. To help yourself focus on interesting controversies and open questions – and not on misconceptions – clarify some of the most common myths.

Timeline Myths

The first myth relates to the timeline: how long will it take until machines are taken far above human-level intelligence? A common misconception is that we know the answer with great certainty.

On the flip side, a favorite counter-myth is that we all know we won’t acquire superhuman AI this century. By way of instance, Ernest Rutherford, arguably the best atomic physicist of the time,” said in 1933 — significantly less than 24 hours before Szilard’s creation of the atomic chain reaction — that atomic power was”moonshine.” The most intense form of the myth is that superhuman AI will not arrive because it is physically not possible. But, physicists are aware that a brain contains quarks and electrons organized to function as a strong computer, which there is no law of physics preventing us from creating more smart quark blobs.

There have been numerous studies asking AI researchers the number of years from now they believe we will have human-level AI with 50% likelihood. These polls have exactly the identical decision: the world’s top experts disagree, therefore we just don’t understand. By way of instance, in this survey of their AI researchers in the 2023 Puerto Rico AI seminar, the typical (median) response was 2045, but a few investigators guessed countless years or even more.

There is also a related myth that those who fear about AI believe it has just a couple of decades away. In reality, the majority of individuals on document fretting about superhuman AI suspect it is still at least decades away. However they assert that as long as we are not 100% convinced that it will not occur this century, it is wise to begin security research today to get ready for the eventuality. A number of the security problems related to human-level AI are so difficult that they might take decades to address. So it is wise to begin exploring them now instead of the evening before some developers drinking Red Bull opt to change one on.


Another frequent misconception is that the only folks voicing concerns concerning AI and recommending AI security study are luddites who do not know a lot about AI. A related misconception is that encouraging AI security research is enormously controversial. In reality, to encourage a small investment in AI security study, individuals do not have to be convinced that dangers are large, only non-negligible — as a small investment in house insurance is warranted with a non-negligible likelihood of the house burning down.

Also read: The 15 Best E-Commerce Marketing Tools

The interesting controversies

Role Of Artificial Intelligence In Smart Meters


Global transformations are taking place to get the most out of the data because of the widespread deployment of smart meters, which present more than 16 million in the United Kingdom.

Aim of researchers and utilities are Timely and accurate billing, a better understanding of home energy use, easing the transition to renewable energy and electric vehicles, and improved management of electricity generation and distribution. By reducing unnecessary energy use, households and utilities can cut costs and achieve goals related to energy efficiency and climate change. But how ? Artificial intelligence is the solution.

Emerging technologies like Artificial Intelligence have a role in industries. Artificial Intelligence, Machine Learning, Deep Learning, Reinforcement Learning, and other related fields have received attention from businesses recently.

Today, we can use the power of Artificial Intelligence to analyze data from smart meters with the help of machine learning to reduce our energy consumption. Time-series machine learning algorithms make it simple for an algorithm to “learn” how much energy is consumed from historical data and predict the future. That can help the Artificial Intelligence engine decide demand-response and ensures security.

Learning Objectives

1. We will discuss the need for Smart meters and why it is necessary.

2. We will take the Smart energy csv file and do the time series analysis of the number of Smart meters applied by Large and small suppliers domestically. We will take the period analysis by fbprophet and ARIMA.

In India, work is in progress. In England, all houses and others working places will be available by the end of 2025.

This article was published as a part of the Data Science Blogathon.

Table of Contents Why do we Need Smart Meters?

Often they have questions in mind.

1. How to reduce electricity bills?

2. How to stop the coming of inaccurate bills?

Smart meters are the answers to all the questions.

Electronic or Smart meters are self-automatic reading machines that read the meter reading and show us the data stored in rupees, pounds, dollars, or pence.

Smart meters cover gas, water, and electricity. It helps to take readings of gas and electricity, send them to the suppliers, and show off the amount of gas and electricity used by the device installed in your home or office and how you can reduce your energy bill.

The use of Smart meters helps reduce your bills. Reduction in CO2 emissions and accurate readings.

Now no Complaints about inaccurate or missing bills.

Why are Smart Meters Helpful to Customers and Suppliers?

1. No more inaccurate bills: A smart meter design helps to know how much you have used electricity and gas and how much is remaining. It will display the days left based on the daily use of electricity and gas. In other words, if you have a non-smart meter, then based on the use of electricity and gas in the past, Suppliers issue bills that can be inaccurate. With the help of a Smart meter, false billing gets stopped, and you know how to limit your use of electricity and gas.

2. By Smart meters, more data will come out that can help the suppliers to know about the supplies of energy customers build and make a profit. For example, if the energy bill exceeds suddenly at night will mean some fault or applications were running at night.

3. Benchmarking analysis is another way to know your competitors in the same field.

Altogether Smart meters are necessary options to be taken to reduce CO2 emissions into the atmosphere.

With the help of the SMILE project in the United Kingdom, the energy usage by a Smart meter in care-taking homes or patients will let us know when the person uses which day electricity more, leading to the observation of health in the disabled and elderly.

Smart meters will let us know when the person uses which day electricity more, which will detect health issues in the disabled and elderly.

How can a Smart Meter be Acquired?

Advanced metering infrastructure is a system with controller, Suppliers that controls your energy usage by communicating in two ways. It is not the like an Automated meter system. It conveys two ways by sending home the energy used at a particular time. It is a host to collect the reading by broadband over a power line or landline and then sends it to the meter data management system.

A Distribution Company or Discom is a business that delivers electricity to customers. These companies don’t make electricity but buy electricity from the people who make it and sell it to people. Discoms are the owners of the grids that you see all over your city. These are part of the electricity chain with two companies named GENCOs that generate and TRANSCOs that transmit the electricity.

Discoms are two types owned by the state and private. By the state, like Kerala Electricity Board and Karnataka Power Corporation Limited, and private discoms, like Tata Power, BSES Rajdhani, and Reliance.

About 1.7 million Smart meters got installed by Energy Efficiency Service Limited, IntelliSmart, and other agencies.

facilitate the implementation of smart meters through a BOOT (Build, Own, Operate, Transfer) model. Energy Efficiency Services Ltd (EESL) is an Indian government energy service company.

The government has mandated that energy providers provide their customers with smart meters.

Contact your energy provider for smart meter installation at a time and date that work best for you. It will not cost you anything.

According to our most recent analysis, the country is gaining more benefits from the rollout than it implemented.

What are Solar Panels?

Solar panels comprise solar cells of silicon, phosphorous (negative charge), and boron (positive) in layers.

The Photovoltaic effect is where the photons from solar panels start an electric current that hits the solar panel’s surface and releases electrons from orbits into the solar cells’ electric field, which pulls these free electrons into a directional Current.

A home roof space often has enough room for the number of solar panels needed to generate enough electricity to meet all of its needs. Any unwanted goes to the power grid, which saves money on electricity bills at night.

Solar Panels and Smart Meters

Energy Suppliers

There are two meters, one for electricity and one for gas; Smart meters will take their places. An In-Home Display (IHD) is a handheld device attached to the home and is simple to use.

We took the data of England’s domestic and non-domestic gas and electricity meters by small and all suppliers for September 2023.

Engineers will have better information about what caused power outages and will be able to detect them much more quickly. They will be able to complete repairs more quickly and affordably.

Additionally, smart meters are contributing to our decrease in reliance on imported fossil fuels. We can save money using energy during off-peak hours or when there is more clean electricity. Some customers have even got an award for using electricity on windy days.

The Sum of Small suppliers for gas meters was highest for 2023 at 137450, followed by 2023 and 2023. The maximum amount of applying of gas meters is in 2023 by the large suppliers.

The suppliers of large gas meters were more in 2023, 2023, and 2023. The suppliers for small electricity meters are more in 2023 and 2023.

Time Series data are the data that make changes or move a period, and to know the future data values, we need Time Series forecasting.

Time Series Analysis

A procedure of analyzing a sequence of data values collected over a specific time is called time series analysis.

Time Series data analysis puts insight into seasonal patterns, trends, and the future that can help Electricity and Gas Suppliers to make profits.

Models and Techniques for Time Series Analysis

ARIMA (Autoregressive, Moving Average model): It takes past values to predict the future.

Autoregressive – An autoregression model assumes that previous time step observations can predict the value at the subsequent time step.

Integrated – The difference between the new data values and the previous values takes their place to make the data stationary.

Moving Average –  A moving average takes the arithmetic mean of a particular set of values over a specific period.

Univariate ARIMA- Jenkins model: Ony Single dependent variable like temperature.

The single variable data in the univariate ARIMA model is forecast. For example

            Temp Date 2023-02-01    60 2023-02-02    70 2023-02-03    55

Multivariate ARIMA – Jenkins models: Multiple dependents like temperature and humidity.

The two or more variable data in the multivariate ARIMA model are forecast. For example

            Temp Humidity Date 2023-02-01    60 75.2 2023-02-02    70 60.1 2023-02-03    55 52.3

Time series data analysis involves the following steps.

1. Stationary – To check for seasonal patterns of the data. A series whose properties do not change over time is called a stationary time series. Variance, mean, and covariance are these characteristics. Trends and seasonality are absent from stationary time series.

2. Autocorrelation – Future values are correlated to past values or not.

The relationship between two variables is termed correlation means variables are related to each other.

Positive correlation occurs when both variables change in the same direction (e.g., simultaneously go up or down). A negative correlation occurs when two variables change values in opposite directions (e.g., one goes up and one goes down).

Autocorrelation is the term used to describe the correlation between the variable and itself at earlier time steps.

Interestingly, the time series problem may not be predictable if all lag variables have low or no correlation with the output variable.

For Stationary data, we will use Dickey-Fuller Test.

It will give p-values. If we accept the null theory, the data is stationary, and if we reject it, not Stationary data.

We calculate the rolling mean and the amount of variance (STD) for seven months.

rolling_mean = meter['Large SE meter in SM'].rolling(7).mean() rolling_std = meter['Large SE meter in SM'].rolling(7).std()#import csv

We imported the adfuller from the stats model and passed out the data meter and parameter AIC.

from statsmodels.tsa.stattools import adfuller af = adfuller(meter['Large SE meter in SM'],autolag="AIC") data_out = pd.DataFrame({"Values":[af[0],af[1],af[2],af[3], af[4]['1%'], af[4]['5%'], af[4]['10%']] , "Metric":["Test Statistics","p-value","No. of lags used","Number of observations used","cvalue(1%)", "cvalue (5%)", "cvalue (10%)"]}) print(data_out)

The p-value is more than 0.05, and the critical value is less than the test statistics results. The data is not stationary and has increasing trends.


autoc_lag1 = meter['Large SE meter in SM'].autocorr(lag=1) print("One Month Lag: ", autoc_lag1) autoc_lag3 = meter['Large SE meter in SM'].autocorr(lag=3) print("Three Months Lag: ", autoc_lag3) autoc_lag6 = meter['Large SE meter in SM'].autocorr(lag=6) print("Six Months Lag: ", autoc_lag6) autoc_lag9 = meter['Large SE meter in SM'].autocorr(lag=9) print("Nine Months Lag: ", autoc_lag9) One Month Lag: 0.9916188803959796Three Months Lag: 0.9549079337204182 Six Months Lag: 0.9261679644654887 Nine Months Lag: 1.0

The results show data is highly correlated.


from statsmodels.tsa.seasonal import seasonal_decompose dec = seasonal_decompose(meter['Large SE meter in SM'],model='additive', period=5) dec.plot() meter['Year'] = meter.indexdf = pd.DataFrame() df['ds'] = meter['Year']df['y'] = meter['Large SG meters in SM'] df.head()split_date = 2023df_train = df.loc[df.ds <= split_date].copy() plt.plot(df_test, color = "red")plt.title("Train/Test split for Suppliers") plt.ylabel("Suppliers") plt.xlabel('Year-Month')sns.set() csv from pmdarima.arima import auto_arima df['Year'] = df.index split_date = 2023 df_train = df.loc[df.ds <= split_date].copy() model = auto_arima(df_train['y'], trace=True, error_action='ignore', suppress_warnings=True)['y']) forecast = model.predict(n_periods=len(df_test['y'])) forecast = pd.DataFrame(forecast,index = df_test['y'].index,columns=['Prediction']) ARIMA(1,0,1)(0,0,0)[0] intercept : AIC=inf, Time=0.22 sec ARIMA(0,0,0)(0,0,0)[0] intercept : AIC=153.606, Time=0.26 sec ARIMA(1,0,0)(0,0,0)[0] intercept : AIC=155.409, Time=0.09 sec ARIMA(0,0,1)(0,0,0)[0] intercept : AIC=inf, Time=0.14 sec ARIMA(0,0,0)(0,0,0)[0] : AIC=154.292, Time=0.00 sec Best model: ARIMA(0,0,0)(0,0,0)[0] intercept Total fit time: 0.700 seconds plt.plot(forecast) from math import sqrtfrom sklearn.metrics import mean_squared_error rms = sqrt(mean_squared_error(df_test['y'],forecast)) print("RMSE: ", rms)RMSE: 6731165.9583494775 def mape(ac, pre): ac, pre = np.array(actual), np.array(pre) return np.mean(np.abs((ac - pre) / actual)) * 100 mape(df['y'], forecast)4098.346771463499 Time Series Analysis of Large supplier’s Gas Meters by Fbprophet

Fbprophet, an open-source library developed or built by Facebook, is used for time series analysis. It requires two columns where ds refers to the year and the y column to the data variable.

Why Fbprophet?

It hands out several outliers and null values and shows results in seconds.

The user can manually add seasonality and holiday values.


from sklearn.metrics import mean_squared_error, mean_absolute_errorfrom chúng tôi import add_changepoints_to_plot meter=pd.read_csv("Meter_energy_domestics.csv") meter=meter.fillna(0)meter.head() sns.boxplot(x =meter['Large SG meters in SM']) meter=meter.set_index("Year") meter.plot()#import csv

We then converted the Year column into the ds column and took the y column.

meter=meter.reset_index()df = pd.DataFrame()df['ds'] = meter['Year'] df['y'] = meter['Large SG meters in SM']df.head()split_date = 2023 m = Prophet() = m.make_future_dataframe(periods=365) forecast = m.predict(df_test) forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper', 'trend', 'trend_lower', 'trend_upper']].tail() fig1 = m.plot(forecast)#import csv m.plot_components(forecast) print("MSE:", mean_squared_error(y_true = df_test["y"], y_pred = forecast['yhat'])) print("MAE:", mean_absolute_error(y_true = df_test["y"], y_pred = forecast['yhat'])) MSE: 788768128821.7513MAE: 790450.4288312361 print("MAPE: ", mean_abs_perc_err(y_true = (df_test["y"]), y_pred = (forecast['yhat']))) MAPE: 57.08464486655384#import csv

Time series analysis of Large supplier’s gas meter in Traditional mode by fbprophet.

df = pd.DataFrame() df['ds'] = meter['Year'] df['y'] = meter['Large SG meters in TM'] df.head() split_date = 2023 df_train = df.loc[df.ds <= split_date].copy() m = Prophet() future = m.make_future_dataframe(periods=365) forecast = m.predict(df_test) forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper', 'trend', 'trend_lower', 'trend_upper']].tail() fig1 = m.plot(forecast)#import csv

Large Suppliers’ gas meter non-smart

df = pd.DataFrame()df['ds'] = meter['Year'] df['y'] = meter['Large SG meters non-smart'] df.head()split_date = 2023 m = Prophet() = m.make_future_dataframe(periods=365) forecast = m.predict(df_test) forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper', 'trend', 'trend_lower', 'trend_upper']].tail() fig1 = m.plot(forecast)#import csv

Large Suppliers’ Electricity meters in Smart mode

df = pd.DataFrame()df['ds'] = meter['Year']df['y'] = meter['Large SE meter in SM']df.head()split_date = 2023 m = Prophet() = m.make_future_dataframe(periods=365) forecast = m.predict(df_test) forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper', 'trend', 'trend_lower', 'trend_upper']].tail() fig1 = m.plot(forecast)#import csv Conclusion

We discuss the few benefits of Smart meters, their uses, and how they can be the foundation of the future in health-related issues. We further discussed Solar Panel and went for an analysis of the data and its future predictions.

Key Points

Before Smart meters, there was often a complaint about inaccurate or missing bills.

1. The smart meter helps reduction of electricity bills and misinformation about electricity bills.

2. Facebook created Prophet, an open-source library for automatically forecasting univariate time series data.

3. An analysis of the data and its future predictions by fbprophet and ARIMA.

4.  fbprophet results show a lower MAPE value.

5. The analysis results show the future of Large suppliers’ gas meters in Smart mode rises. The large suppliers’ gas meters non-smart will decrease, traditionally, with similar values.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion. 


Artificial Intelligence Just Discovered Two New Exoplanets

A machine learning technique called a neural network has identified two new exoplanets in our galaxy, NASA scientists and a Google software engineer announced today, meaning that researchers now know about two new worlds thanks to the power of artificial intelligence.

Discovering new exoplanets—as planets outside our solar system are called—is a relatively common occurrence, and a key instrument that scientists use to identify them is the Kepler Space Telescope, which has already spotted a confirmed 2,525 exoplanets. But what’s novel about this announcement is that researchers used a AI system to spot these two new worlds, now dubbed Kepler-90i and Kepler-80g. The planet known as 90i is especially interesting to astronomers, as it brings the total number of known planets orbiting that star to eight, a tie with our own system. The average temperature on 90i is thought to be quite balmy: more than 800 degrees Fahrenheit.

Just as exoplanet discoveries are common, so too are neural networks, which is software that learns from data (as opposed to a program that have had rules programmed into it). Neural networks power language translation on Facebook, the FaceID system on the new iPhone X, and image recognition on Google Photos. A classic example of how a neural network learns is to consider pictures of cats and dogs—if you feed labeled images of cats into a neural network, later it should be able to identify new images that it thinks has cats in them because it has been trained to do so.

“Neural networks have been around for decades, but in recent years they have become tremendously successful in a wide variety of problems,” Christopher Shallue, a senior software engineer at Google AI, said during a NASA teleconference Thursday. “And now we’ve shown that neural networks can also identify planets in data collected by the Kepler Space Telescope.”

Astronomers need tools like telescopes to search for exoplanets, and artificial intelligence researchers need vast amounts of labeled data. In this case, Shallue trained the neural network using 15,000 labeled signals they already had from Kepler. Those signals, called light curves, are measures of how a star’s light dips when a planet orbiting it passes between the star and Kepler’s eye, a technique called the transit method. Of the 15,000 signals, about 3,500 were light curves from a passing planet, and the rest were false positives—light curves made by something like a star spot, but not an orbiting planet. That was so the neural network could learn the difference between light curves made by passing planets and signals from other phenomena.

Eventually, Shallue and his collaborator, Andrew Vanderburg, a NASA Sagan postdoctoral fellow at the University of Texas, Austin, turned the neural network loose on data from Kepler that wasn’t in its original training set. It sifted through data from 670 star systems, focusing on weak signals that could possibly represent a previously undiscovered planet. And sure enough, they found two new worlds.

“Machine learning really shines in situations where there is too much data for humans to examine for themselves,” Shallue said.

Looking through the weak signals from those 670 stars and finding two planets was “proof of concept” that their neural network works, he says. Their next step is to use it on much more data: signals from about 150,000 additional stars. And Shallue concedes that he’s no an astronomy expert, which is why he collaborated on the project with Vanderburg.

While artificial intelligence tools have been used in this kind of research before, “this is the first time a neural network specifically has been used to identify a new expoplanet,” Shallue said during the press conference.

Mary Beth Griggs contributed research to this report.

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