tailieunhanh - Biogas electricity production forecasting in livestock farms using machine learning techniques: A case study in Vietnam

Biogas energy is considered a renewable energy source. The efficient usage of biogas resources can help reduce greenhouse gas emission, especially methane, generate electricity to power farms’ loads, and decrease load demand on grids. | P-ISSN 1859-3585 E-ISSN 2615-9619 SCIENCE - TECHNOLOGY BIOGAS ELECTRICITY PRODUCTION FORECASTING IN LIVESTOCK FARMS USING MACHINE LEARNING TECHNIQUES A CASE STUDY IN VIETNAM DỰ BÁO SẢN LƯỢNG ĐIỆN KHÍ SINH HỌC Ở CÁC TRANG TRẠI CHĂN NUÔI SỬ DỤNG CÁC THUẬT TOÁN HỌC MÁY MỘT NGHIÊN CỨU TẠI VIỆT NAM Nguyen Duy Hieu1 Nguyen Vinh Anh1 Hoang Anh2 Hoang Duc Chinh1 DOI https ABSTRACT 1. INTRODUCTION Biogas energy is considered a renewable energy source. The efficient usage Energy is the fuel of civilization. It is part of the of biogas resources can help reduce greenhouse gas emission especially fundamental high-resolution foundation that upholds the methane generate electricity to power farms loads and decrease load demand lower-resolution more abstract functioning of our society on grids. We first present the data acquisition scheme of self-developed biogas and it was estimated that the total electricity consumption generation systems complete with a description of the farm architecture and of the world was around 25 TWh in 2019 1 . The demand load estimation. Then with the necessary data collected five machine learning for energy is ever-growing with primary energy having techniques are then explored and adopted to process the data and forecast experienced an estimated 31-exajoule increase in 2021 2 . energy production at several livestock farms in practice. Comparisons are made Although most of the energy demand was met with fossil among these techniques which includes RNN MLP polynomial regression fuel which accounted for 59 of 2021 s energy generated decision trees and random forest regression to evaluate the accuracy of the renewable energy had nevertheless assimilated a predictions. It was concluded from the comparisons that Polynomial Regression considerable 13 share of global power generation which performed the best in predicting the energy production at the hog farm while remarkably was higher than that of nuclear energy which .