EGD-SNet: A computational search engine for predicting an end-to-end machine learning pipeline for Energy Generation & Demand Forecasting

Load forecasting avoids energy wastage by accurately estimating the future quantity of energy generation and demand. Existing load forecasting approaches do not utilize the potential of feature selection and dimensionality reduction approaches that remove irrelevant/redundant features and improve the performance of machine learning (ML) regressors. This research presents an end-to-end framework named Energy Generation and Demand forecasting Search Net (EGD-SNet) capable of predicting energy generation, demand and temper-ature in multiple regions. EGD-SNet framework contains 13 different feature selection and 11 dimensionality reduction algorithms along with 10 most widely used ML regressors. It makes use of Particle Swarm Optimizer (PSO) to smartly train regressors by finding optimal hyperparameters. Further, it has potential to design an end to end pipeline by finding appropriate combination of regressor and feature selection or dimensionality reduction approaches for precisely predicting energy generation or demand for a particular regional data based on the characteristics of data. EGD-SNet as web service is accessible here. http://111.68.102.19:8000/

Automated summary

Load forecasting is important to avoid energy wastage by accurately estimating future energy generation and demand. However, existing approaches lack the potential of feature selection and dimensionality reduction, which can improve machine learning regressors' performance. This research introduces an end-to-end framework named EGD-SNet that predicts energy generation, demand, and temperature in multiple regions.