A novel electricity load forecasting based on probabilistic least absolute shrinkage and selection operator-Quantile regression neural network

Abstract

Predicting energy consumption is challenging since forecast accuracy is heavily impacted by several external elements, including the economy, community, environment, and sustainable energy, including wind, solar, and hydro energy. The most crucial aspect in successfully predicting electrical power demand is to gather crucial information about those external factors in the vast amount of data provided by the smart grid. The Least Absolute Shrinkage and Selection Operator-Quantile Regression Neural Network (LASSO-QRNN) is a strategy for probability density prediction proposed in this article. First, using LASSO regression, the primary features are determined from external factors impacting electrical power utilization. The LASSO-Quantile regression NN approach is then developed to forecast annual electrical power consumption. In the years that followed, various quantiles were employed to gauge electrical power demand, and the outcomes were assessed. The LASSO-QRNN approach also includes a kernel density evaluation that, in the event of a one-value forecast, will display a probability distribution. The experimental assessments from the Guangdong database in China and the California real-world databases in the US have evaluated the forecasting precision. The results of the numerical study demonstrate that, in comparison to Quantile Regression and Kernel Density, Box-Cox transformation, the provided model provides superior accuracy for the prediction of electrical power demand. Deep Generative Architecture, K-means Clustering, a Quantile Regression Neural Network, expert prediction, and fuzzy Bayesian theory. LASSO-Quantile regression neural network can handle the higher dimension information in electrical power usage prediction besides the more accurate outcomes.