Abstract
Load forecasting has become one of the major areas of research in electrical engineering. Short term load forecasting (STLF) is essential for power system planning and economic load dispatch. A variety of mathematical methods has been developed for load forecasting. This paper discusses the influencing factors of STLF and an artificial intelligence (AI) based STLF model for MGVCL load. It also includes comparison of various AI models. Our main objective is to develop the best suited model for MGVCL, by critically evaluating the ways in which the AI techniques proposed are designed and tested.
Highlights
Electric load forecasting is the process used to forecast future electric load from the given historical load and weather information
Short term load forecasting mainly aims at one hour to one week forecast
Application of artificial intelligence (AI) techniques like neural networks and adaptive neuro fuzzy interface systems is an advanced approach for accurate short term load forecasting
Summary
Electric load forecasting is the process used to forecast future electric load from the given historical load and weather information. Application of artificial intelligence (AI) techniques like neural networks and adaptive neuro fuzzy interface systems is an advanced approach for accurate short term load forecasting. B. Learning Algorithm ANNs work through optimized weight values [2]. The trained neural network, with the updated optimal weights, should be able to produce the output within desired accuracy.
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