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

Faiza Mehmood, Muhammad Usman Ghani, Hina Ghafoor, Rehab Shahzadi, Muhammad Nabeel Asim and Waqar Mahmood

Applied Energy, 2022, vol. 324, issue C, No S0306261922010388

Abstract: 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 temperature 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/

Keywords: Load forecasting; Computational methodologies; Machine learning; Feature selection; Dimensionality reduction (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (3)

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DOI: 10.1016/j.apenergy.2022.119754

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