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Accurate load forecasting is an essential task for the utilities for proper operation and effective energy trading in markets. Distribution companies are paying huge amount in the form of penalties due to deviation in actual power consumption. This can be avoided with accurate load forecasting tools. In this chapter, a complete procedure for short-term load forecasting using an artificial neural network (ANN) model is presented. The entire data collection used to train and test the ANN model was gathered from the Indian Energy Exchange (IEX) and can be seen at https://data.mendeley.com/datasets/jxm8d4w4cv/1 . Comparison of the suggested model with other machine learning models validates it. Comparatively speaking, the constructed ANN model can predict the load with a lower error of 0.0017.

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Short Term Load Forecasting for Effective Trading in Energy Market Using Artificial Neural Networks and ADAM Optimizer

  • 章节

  • 发表日期：

    2025年

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  • Venkataramana Veeramsetty,
  • Nikitha Baddam,
  • Ramana Pilla,
  • Surender Reddy Salkuti

摘要

Accurate load forecasting is an essential task for the utilities for proper operation and effective energy trading in markets. Distribution companies are paying huge amount in the form of penalties due to deviation in actual power consumption. This can be avoided with accurate load forecasting tools. In this chapter, a complete procedure for short-term load forecasting using an artificial neural network (ANN) model is presented. The entire data collection used to train and test the ANN model was gathered from the Indian Energy Exchange (IEX) and can be seen at https://data.mendeley.com/datasets/jxm8d4w4cv/1 . Comparison of the suggested model with other machine learning models validates it. Comparatively speaking, the constructed ANN model can predict the load with a lower error of 0.0017.