Statistical and Machine Learning Approaches for Electrical Energy Forecasting

Abstract

With renewable energy being aggressively integrated into the grid, energy supplies are becoming vulnerable to weather and the environment, and are often incapable of meeting population demands at a large scale if not accurately predicted for energy planning. Understanding consumers' power demands ahead of time and the influences of weather on consumption and generation can help producers generate effective power management plans to support the target demand. In addition to the high correlation with the environment, consumers' behaviors also cause non‐stationary characteristics of energy data, which is the main challenge for energy prediction. In this survey, we perform a review of the literature on prediction methods in the energy field. So far, most of the available research encompasses one type of generation or consumption. There is no research approaching prediction in the energy sector as a whole and its correlated features. We propose to address the energy prediction challenges from both consumption and generation sides, encompassing techniques from statistical to machine learning techniques. We also summarize the work related to energy prediction, electricity measurements, challenges related to energy consumption and generation, energy forecasting methods, and real‐world energy forecasting resources, such as datasets and software solutions for energy prediction.This article is categorized under: Application Areas > Industry Specific Applications Technologies > Prediction Technologies > Machine Learning