BIOGAS ELECTRICITY PRODUCTION FORECASTING IN LIVESTOCK FARMS USING MACHINE LEARNING TECHNIQUES: A CASE STUDY IN VIETNAM

Abstract

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. We first present the data acquisition scheme of self-developed biogas
generation systems, complete with a description of the farm architecture and
load estimation. Then, with the necessary data collected, five machine learning
techniques are then explored and adopted to process the data and forecast
energy production at several livestock farms in practice. Comparisons are made
among these techniques, which includes RNN, MLP, polynomial regression,
decision trees and random forest regression, to evaluate the accuracy of the
predictions. It was concluded from the comparisons that Polynomial Regression
performed the best in predicting the energy production at the hog farm, while
random-tree-based methods performed the worst.