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A new interval pbkp_rediction methodology for short-term electric load forecasting based on pattern recognition

Abstract:

Demand pbkp_rediction has been playing an increasingly important role for electricity management, and is fundamental to the corresponding decision-making. Due to the high variability of the increasing electrical load, and of the new renewable energy technologies, power systems are facing technical challenges. Thus, short-term forecasting has crucial utility for generating dispatching commands, managing the spot market, and detecting anomalies. The techniques associated with machine learning are those currently preferred by researchers for making pbkp_redictions. However, there are concerns regarding limiting the uncertainty of the obtained results. In this work, a statistical methodology with a simple implementation is presented for obtaining a pbkp_rediction interval with a time horizon of seven days (15-min time steps), thereby limiting the uncertainty. The methodology is based on pattern recognition and inferential statistics. The pbkp_redictions made differ from those from a classical approach which pbkp_redicts point values by trying to minimize the error. In this study, 96 intervals of absorbed active power are pbkp_redicted for each day, one for every 15 min, along with a previously defined probability associated with the real values being within each obtained interval. To validate the effectiveness of the pbkp_redictions, the results are compared with those from techniques with the best recent results, such as artificial neural network (ANN) long short-term memory (LSTM) models. A case study in Ecuador is analyzed, resulting in a pbkp_rediction interval coverage probability (PICP) of 81.1% and pbkp_rediction interval normalized average width (PINAW) of 10.13%, with a confidence interval of 80%.