Trending December 2023 # Are You Staffing Your Artificial Intelligence Teams Right? # Suggested January 2024 # Top 14 Popular

You are reading the article Are You Staffing Your Artificial Intelligence Teams Right? updated in December 2023 on the website We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested January 2024 Are You Staffing Your Artificial Intelligence Teams Right?

Here’s a guide to finding the right resources for your AI teams for maximum output.

Here’s a guide to staff your Artificial intelligence – seems like there is no stopping it. The word echoes in the tech industry with more companies embracing this disruptive technology. There are millions of articles on the web and many on our website which talk in-depth about how revolutionary AI is. The sheer diversity and complexity of AI projects at times like now where rapid production is required to meet the pandemic demands are creating a need to identify key AI roles and finding the right personnel for the job.  

What Is The Challenge?

Organizations face challenges in

It’s A Team Effort

To successfully run the operations and leverage artificial intelligence initiatives, companies need to create diverse AI roles and skills. “Artificial intelligence is a team sport. On their AI team, CIOs and technology innovation leaders need to have data scientists, data engineers and complement the team with artificial intelligence architects and machine learning engineers. Together they can envision, build, deploy, and operationalize an

The Right Resources

Here’s a guide to staff your Artificial intelligence – seems like there is no stopping it. The word echoes in the tech industry with more companies embracing this disruptive technology. There are millions of articles on the web and many on our website which talk in-depth about how revolutionary AI is. The sheer diversity and complexity of AI projects at times like now where rapid production is required to meet the pandemic demands are creating a need to identify key AI roles and finding the right personnel for the job.Organizations face challenges in leveraging artificial intelligence projects due to the lack of required skills, collaboration, tools, and the ability to create and manage a dynamic, production-level AI pipeline. According to Gartner, by 2023, 50% of IT leaders will find it tough to move their artificial intelligence products past proof of concept (POC) to a production-grade maturity. This is a high failure rate, and to counter this companies need to create the right AI roles for success. “In many organizations, data scientists are still wearing too many hats due to a death of talent across other roles,” said Arun Chandrasekaran, VP Analyst at chúng tôi successfully run the operations and leverage artificial intelligence initiatives, companies need to create diverse AI roles and skills. “Artificial intelligence is a team sport. On their AI team, CIOs and technology innovation leaders need to have data scientists, data engineers and complement the team with artificial intelligence architects and machine learning engineers. Together they can envision, build, deploy, and operationalize an end-to-end machine learning / artificial intelligence pipeline.” said Arun. Artificial intelligence teams in any business setup should not operate in isolation. The teams need to collaborate with domain experts, IT experts, and other necessary staff and stakeholders to achieve results that successfully drive AI chúng tôi the success of artificial intelligence projects , finding the right resources and making sure they work in alignment with other business processes is crucial. Two roles that are significant to get the desired result are AI architects and machine learning engineers. The artificial intelligence architect is solely focused on the transformational architectural efforts that AI technology introduces. Their main job role is to orchestrate the deployment and management of systems in production and provide inputs on the capability of machine learning and deep learning models within artificial intelligence’s many disciplines like NLP (natural language processes) or image recognition. Machine learning is one of the most used branches of AI. Hence, organizations are increasingly hiring machine learning engineers as a part of their artificial intelligence teams. These professionals are responsible for moving machine learning solutions in production and optimize the environment for maximum performance and scalability. According to Gartner, by 2023, the role of a machine learning engineer will become one of the fastest-growing roles in the AI industry. Gartner also estimates that in today’s date, there is one machine learning engineer for every 10 data scientists and it will likely increase to 5 and 10 by 2023. “ML engineers need to ensure that AI platforms deliver against technical and business SLA requirements. ML engineers are expected to be the connecting fabric with data scientists from an IT perspective and ensure their ML models run well in production”, said Arun to Gartner.

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The Business Intelligence Analyst Are Right Profession For You

You can now determine if a Business Intelligence analyst is the right career path for you.

Do you want to combine your love for data analytics with the ability to shape and support companies A business Intelligence Analyst is responsible for analyzing large amounts of data in a company to make suggestions for improvement and development?

The Business Intelligence Analyst, just like the Analysts and Data Scientists is in high demand and is the fastest-growing job in the technological and business worlds Business Intelligence Analyst can be the key to a company’s success. They are responsible for supporting an enterprise.

What’s a Business Intelligence Analyst?

Business Intelligence analysts are responsible for acquiring business data in many ways. This includes extracting company data digitally, looking at competitor data, and researching industry trends. The data they collect helps to create a picture of a company’s market competitiveness. A business intelligence analyst analyzes data to create financial and market intelligence reports. These reports help companies make informed decisions and recognize patterns in the market.

It is crucial to have the right combination of technical skills, professional experience, deep knowledge, and education about different software in order to access and evaluate data.

What are Their Roles?

Business intelligence analysts are responsible for gathering and interpreting data and communicating the results to the right people. You can further break down these elements into specific tasks that can all help improve the company’s overall performance. Here’s an example of how a data collection project might look:

Storing the Data: You should save the company’s information on your computer database. This database will need to be kept up to date frequently. You may also need to establish operational procedures to make the system work. You may need to set up operating procedures in order to use the database.

Apt Utilization of The Result: The findings of the investigation can be used to support recommendations for actions that will improve the company’s performance or help it grow.

Preparation of The Analytical Reports: Create analytic reports that summarize findings and distribute them via the existing communication channels to all relevant stakeholders.

You will need to work with people from both within and outside your company to keep up with industry trends. To ensure that data flows are tracked and can be analyzed, summarized, and sent to the correct people, you’ll need to keep an eye on them.

Skills Requirement to Become Business Intelligence Analyst

Business intelligence analysts use data, statistical analysis, and visualization to help create predictive models that will aid them in making better decisions. They often look back at past data to understand the results. You should study the following skills if you wish to become a business intelligence analyst:

Descriptive analytics

Predictive analytics

Data collection methods

A/B testing


Correlation and causation

Regression analysis

Data visualization and interpretation

Communication and problem-solving are also skills you should have.

Analysts in business intelligence may work in many fields including finance and accounting.

Why should One Choose This Role? 1. Opportunity to Communicate and Network

Business Intelligence Analysts (BIA) are expected to work with nearly every stakeholder in a project, including the Project Manager, Client (Primary stakeholders, End-users), Finance, Procurement, Higher Management such as the President, Directors, as well as vendors. Analysts often have the opportunity to travel to clients to learn complicated processes under new and challenging conditions and to expand their network.

2. A Fast-Paced Career

Business intelligence analysts have many responsibilities. They must manage multiple aspects of a project while being flexible and approachable. BIA is a highly-sought profession in any organization.

3. A Potential Progressive Career Path

After you have gained significant experience as a Business Intelligence Analyst for 3-5 years, you can choose your future path. You can specialize in one technology/domain and work as a Functional Analyst or as an IT Business Analyst. This bridges the gap between technology and business. You should also consider Project Management if you are looking to move up in management. They have the required experience and skills.

BIA’s involvement in almost every aspect of a project is unquestionable. Business intelligence analysis is a promising career that will allow you to follow your passions and achieve the success you want.

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4. Respect and Visibility within The Organization

5. Attractive Remuneration

Business analysts receive handsome compensation based on their expertise, talents, or capabilities. This is a highly rewarding job compared to the average global salary for Analysts. An average Business Intelligence Analyst makes $55,000 annually, and earnings increase with experience and performance.

6. Professional Excellence is Acknowledged

An analyst’s recognition and acknowledgment are high because of their expertise in project management and business analysis. Analysts are highly valued for their ability to manage projects and ensure the smooth operation of multiple operations. Analysts are motivated to achieve new heights by the adulation.

7. Over time, Communication and Soft Skills are Developed

Although soft skills are commonly thought to only include adaptability, teamwork, and collaboration, they cover much more.

8. Exposure to A Variety of Disciplines

A Business Analyst’s skills are domain-independent, and they are able to manage tasks from multiple domains. This makes it less common for them to be domain-focused. An analyst can gain exposure and experience in a wide range of areas such as banking, finance, and information technology.

Analysts’ efficiency does not depend on the field in which they work. Instead, they gain experience from different domains, increasing their job suitability and knowledge.

Ibm Artificial Intelligence (Ai) Portfolio Review

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

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.

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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.

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The interesting controversies

Most 5 Foods That Are Killing Your Intelligence

Foods That square measure Killing Your Intelligence, Focus and Brain. Our brain has typically been the foremost necessary organ of our body. it’s the mainframe that controls every and each functioning.

Be it our thinking, concentration, or the beating of the center, everything is wired to the brain. that makes it even additional crucial to require care if what we tend to eat and that we do not.

We often eat heaps of things however sure foods will negatively have an effect on the functioning of our brain that directly impacts our memory, reaction, emotions, mood, and additionally will increase the danger of mental health conditions like insanity.

Most 5 Foods That Are Killing Your Intelligence Sugary Beverages

Colas, juices, energy drinks, soda have been a part of your life. While they taste amazing and kill our thirst for a while, it also causes some serious illness. Sugary drinks easily increase the risk of type 2 diabetes and heart disease.

They also have a negative effect on your brain, and type 2 diabetes is sometimes a cause of increasing Alzheimer’s risk. By the way, we all know that these drinks are high in fructose, causing high blood pressure, fat, obesity, brain inflammation, impaired memory, dementia, and more.

Extra Salty Foods

Most of us know how salty foods affect our blood pressure and immediately affects our heart. The greater quantity of sodium intake influences your cognitive thinking, intellect and hurts your ability to believe.

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We know what excessive use of alcohol could do with our own brains. The negative effects are similar to brain volume, disturbance of hormones, and much more.

Additionally, it contributes to a lack of vitamin B1, which can be associated with inducing brain disease Wernicke’s encephalopathy (a life threatening disease which primarily affects the peripheral and central nervous systems). Which later develops into chronic memory disease Korsakoff’s syndrome because of abuse of alcohol.

Trans Fats

Right from our school days, we are told how trans fats can increase obesity, heart problems and cholesterol.

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Regardless of the popular actuality that smoking is quite detrimental, ingestion of it wreak havoc in the brain which reduces the free flow of glucose, blood, and oxygen round it.

Needless to state how these working are critical it is to your mind. From growth risk of lung cancer, premature aging to tightening the capillaries, (a blood vessel that is quite essential in regards to your mind functioning ).

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.

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