Bootstrap Aggregating Approach to Short-Term Load Forecasting Using Meteorological Parameters for Demand Side Management in The North-Eastern Region of India

Abstract

Electricity is an essential commodity that must be generated in response to demand. Hydroelectric power plants, fossil fuels, nuclear energy, and wind energy are just a few examples of energy sources that significantly impact production costs. Accurate load forecasting for a specific region would allow for more efficient management, planning, and scheduling of low-cost generation units and ensuring on-time energy delivery for full monetary benefit. Machine learning methods are becoming more effective on power grids as data availability increases. Ensemble learning models are hybrid algorithms that combine various machine learning methods and intelligently incorporate them into a single predictive model to reduce uncertainty and bias. In this study, several ensemble methods were implemented and tested for short-term electric load forecasting. The suggested method is trained using the influential meteorological variables obtained through correlation analysis and the past load. We used real-time load data from Nagaland's load dispatch centre in India and meteorological parameters of the Nagaland region for data analysis. The synthetic minority over-sampling technique for regression (SMOTE-R) is also employed to avoid data imbalance issues. The experimental results show that the Bagging methods outperform other models with respect to mean squared error and mean absolute percentage error.

Design strategies towards the enhancement of short-term forecasting in the energy sector

“…Bagging methodologies fit ensemble members on random data subsets that encapsulate all the characteristics of the original series and subsequently aggregate the results through voting or averaging strategies. These methods focus on the reduction of variance and could be easily parallelizable for efficient computation [73].…”