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