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Short term load forecasting using artificial neural networks

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  1. Introduction
  2. Meaning of load forecasting
  3. Importance of STLF
  4. Review of STLF measures
  5. Neutral network and its applications
  6. Program development of STLF
  7. Conclusion and future scope
  8. Bibliography

Today's electric power industry is undergoing many fundamental changes due to the process of Deregulation. In the new market environment, the power system operation will become more competitive. Therefore the Utilities are required to perform optimal planning in order to operate their system efficiently. Therefore the accuracy of future load forecast becomes crucial. The accuracy of the short-term load forecasts has a significant impact on an electric utility's operations and production costs. Many conventional statistical methods such as multiple linear regression, time series, general exponential smoothing etc. have been used for forecasting short-term load. Usually, these techniques are effective for the forecasting of short-term load on normal days but fail to yield good results on those days with special events. Further, because of their complexities, enormous computational efforts are required to produce acceptable results. A short-term load-forecasting (STLF) program that uses an integrated Artificial Neural Network (ANN) approach is capable of predicting load for basic generation scheduling functions, assessing power system security, and providing timely dispatcher information. This PAPER PRESENTS the development of an Artificial Neural Network-based short-term load forecasting (STLF). Keywords: Artificial Neural Network (ANN), Short Term Load Forecasting (STLF), Energy Forecasting

[...] Mehrotra, Mohan and Ranka Artificial Intelligence Neural Network Bart Kosko Modern Power System Analysis Nagrath Kothari Power System Planning R. L. Sulivan McGraw Hill The Art & Science of Protective Relaying Crussell Mason PAPERS Neural Network based Short Term Load Forecasting Model - A. M. Sliaraf, T. T. Lie and H. B. Gooi 1993 IEEE. Short Term Electric Load Forecast Using Artificial Neural Networks Andrew T. Sapeluk, C. Siiheyl Ozveren, Alan P. Birch - 1994 IEEE. Short Term Load Forecasting Using Genetically Optimized [...]

[...] Load Forecasting means prediction of future load on Electrical Power System or part of the system. Load Forecasting is one of the most important power system planning tools. It is very important for the power system to know the load behavior in advance. Load Forecasting, if correct ensures uninterrupted, reliable, secure and economic Electrical energy to the end consumers. Load Forecasting plays a central and dominant role in the economic optimization of electrical power system. The load dispatcher must be in a position to judge the loading on the Electrical Power System on daily basis, weekly basis or yearly basis. [...]

[...] Artificial Neural Networks are also referred to as ?Neural Nets'; ?Artificial Neural Systems'; ?Parallel Distributed Processing Systems' or ?Connectionist Systems'. Neural networks are composed of simple elements operating in parallel. These elements are inspired by biological nervous systems. As in nature, network function is determined largely by connections between elements. One can train neural network to perform particular function by adjusting values of connections (weights) between elements. Commonly neural networks are adjusted, or trained, so that a particular input leads to a specific target output. [...]

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