Power System Planning with Load Forecasting

The power systems are extremely complicated and consists of different interconnected parts. This extensive system has many elements, like generators, stations, transmission lines, and transformers. The primary purpose of this system is to produce and distribute reliable energy to consumers.

The number and behavior of these consumers are changing fast, so power systems must be updated to be able to meet the user's needs. Developing the system to satisfy future needs is called power system planning

We optimize your production processes ensuring you are more able to make efficient use of machines, avoid production downtime and increase the quality of your products. To do this we use the most advanced Big Data and AI technology available. We use your company’s machine and production data as a database for our analyses which we can also access and process for you if needed. We also build on your own experts’ knowledge and experience and incorporate it into our analyses.

anaplatform provides elastic, cost-effective compute and storage capacity to run any and all complex energy operations—including demand response and distributed energy forecasting.

Why You Need AI For Load Forecasting ?

Conventional approaches have been used to forecast feeder/substation electric demand manually with the help of spreadsheets, charts, and tables, but these methods cannot provide accurate and reliable forecasts. With the advent of artificial intelligence and machine learning (AI/ML) techniques, researchers are now improving the modeling of ST-ELF. These approaches typically involve AI/ML and data science expertise to build and maintain ST-ELF models. Today, these skillsets typically fall outside of the areas of expertise of many utilities.

What is anaplatform Laod Forecasting Service ?

anaplatform Load Forecast is a fully managed service that uses advanced machine learning techniques to deliver highly accurate forecasts. This methodology paves the way for utilities to generate accurate ST-ELF models without incurring significant investment in AI/ML and data scientists

How we do It ?

If Machine Learning is to be applied to production, we develop a systematic procedure model by integrating relevant energy domain and specialist knowledge into an automated form. This is then applied to the data analysis by adapting learning procedures to typical production issues. Our long-term experience creating data analyses and using technology building blocks for Load Forecasting combined with our intensive decentralized learning research gives us a technological advantage when production-ready solutions need to be developed quickly.

Our Load Forecasting Solutions

Very short term load forecast

The timing for Very Short Term Load Forecast (VSTLF) starts from several seconds to several minutes. VSTLF is used in economic dispatch and load frequency control and predicts the load from thirty seconds to thirty minutes ahead.

Short-term load forecast

Power system operators use Short Term Load Forecasting (STLF) to overcome the overloading and increasing the reliability of the power system. The timing for STLF starts from an hour to a month. STLF is very crucial for operators, and the information gained from STLF is extremely valuable. The short term is the primary interest of this paper, so the main methods for STLF and main elements that affect STLF are shortly discussed in the next part.

Short-term forecasts are usually for periods covering one hour to one week. It plays an important role in the day-today operations of a utility such as level and selection of generating capacity to meet actual power demand, economic dispatch and load management.

In residential short-term load forecasting (STLF), future power consumption is projected by applying a preestablished relationship between power load and its influence factors, or by dynamically assessing historical data and adapting the correlation of the influence factor—namely, time and/or weather—with the load.

Medium-term load forecast

This type of forecasting varies between one month to one year. Most of the generator units use a Medium Term Load Forecast (MTLF) to predict the amount of needed fuel, but on the bigger scale, all power system operators use MTLF to anticipate the future expansion, and material purchases.

Long-term load forecast

Long- Term Load Forecast (LTLF) anticipates load for more than one year. Long-term forecasting is used for preparing the needs of the station for the future, from fuel to workers. LTLF helps operators to make decisions for long periods.

In order to maintain steady supply of electricity to meet demand; early and accurate forecasts are helpful in formulating load management strategies to cope with emerging economic scenarios, which can be merged with the developmental plan of the region

Load Analysis Types

The need for medium and long term load forecasting is growing by the increase of the use of renewable energies. The future needs more accurate and secure grids. Short term forecasting methods were mostly used in medium and long term forecasting with sufficient accuracy..

Medium and long term method needs more amount of data and training. There are three main methods for the medium and long term:

• Trend Analysis (TA)
• End-Use Analysis (EUA)
• Econometric Analysis (EA)

Trend Analysis (TA)

TA uses the consumption of the past year to forecast consumption for next year. The basic idea for TA is that possible future developments are predictable by changes in the past.

End-Use Analysis (EUA)

Generally, the people who are using a finished commodity were called end-users. The consumption of all commodities is related to these people, and electricity is not an exception, and this is the main idea of EUA. Data from the past is used to find out how much electricity each consumer used in each device. The predicted consumption for each residential user is equal to the amount of consumption for each device multiplied by the number of devices. The result is an estimation of the electricity demand in the future for a residential area. EUA could be inaccurate because this method assumed that the consumers' behavior is constant over the forthcoming years. Furthermore, this assumption could not be valid for extensive periods. The second cause of inaccuracy is that this method uses current data. The same approach could also be used for commercial and industrial users.

Econometric Analysis (EA)

The econometric analysis makes use of both previous methods. Complicated mathematical equations show the relationships between demand and the factors that impact it. These equations were used to find the value of essential factors. So the accuracy of the model depends on the accuracy of these factors.

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Investing in our Power System Planning solution will help you optimize your portfolios, reduce risk exposure, and ultimately drive better returns on investment. Contact us today to learn more about how we can help you take your power system management to the next level.