Diving Deep into Short-Term Electricity Load Forecasting: Comparative Analysis and a Novel Framework

In this article, we present an in-depth comparative analysis of the conventional and sequential learning algorithms for electricity load forecasting and optimally select the most appropriate algorithm for energy consumption prediction (ECP). ECP reduces the misusage and wastage of energy using mathematical modeling and supervised learning algorithms. However, the existing ECP research lacks comparative analysis of various algorithms to reach the optimal model with real-world implementation potentials and convincingly reduced error rates. Furthermore, these methods are less friendly towards the energy management chain between the smart grids and residential buildings, with limited contributions in saving energy resources and maintaining an appropriate equilibrium between energy producers and consumers. Considering these limitations, we dive deep into load forecasting methods, analyze their performance, and finally, present a novel three-tier framework for ECP. The first tier applies data preprocessing for its refinement and organization, prior to the actual training, facilitating its effective output generation. The second tier is the learning process, employing ensemble learning algorithms (ELAs) and sequential learning techniques to train over energy consumption data. In the third tier, we obtain the final ECP model and evaluate our method; we visualize the data for energy data analysts. We experimentally prove that deep sequential learning models are dominant over mathematical modeling techniques and its several invariants by utilizing available residential electricity consumption data to reach an optimal proposed model with smallest mean square error (MSE) of value 0.1661 and root mean square error (RMSE) of value 0.4075 against the recent rivals.

Automated summary

This study compares the performance of traditional and sequential learning algorithms in electricity load forecasting and presents a new ECP framework that utilizes deep sequential learning models to achieve optimal model optimization.