Work

Ensemble Technique-Based Short-Term Supply and Demand Forecasting with Features Selection Approach in Decentralized Energy Systems

Year: 2024

Type: article

Abstract: Decentralized energy systems (DESs) present a paradigm shift toward more sustainable and resilient electricity networks with the increasing integration of renewable energy sources. Accurately forecasting energy supply and electricity demand is crucial for the efficient operation of DES. This paper focuses on accurate short-term energy supply and demand forecasting using machine learning (ML) algorithms across different seasons within small-scale DES, including photovoltaic (PV) generation, wind generation, and load demand. Initially, we develop multiple base model ML algorithms including long short-term memory (LSTM), convolutional neural networks (CNN), eXtreme gradient boosting (XGBoost), and recurrent neural networks (RNN) with feature selection approaches to improve forecasting accuracy and reduce model complexity. These base models leverage key temporal and spatial features and seasonal variations process to improve the model forecasting accuracy and reduce overfitting across different seasons. To further reduce forecast errors, we propose robust ensemble forecasting techniques including simple averaging (SA), weighted averaging (WA), and Stacking. In ML algorithms, the ensemble forecasting technique combines the multiple base models’ forecasts to produce a more accurate and robust final forecast by leveraging the strengths and compensating for the weaknesses of individual base models. Finally, numerical simulations are conducted using Python, Keras, and TensorFlow libraries to develop, train, evaluate, and validate the effectiveness and accuracy of the developed forecasting models and the proposed ensemble forecasting techniques. The results demonstrate that the proposed approach offers a robust solution for short-term supply and demand forecasting problems in DES. The work is both novel and effective from the perspectives of application, ML algorithms combination, and performance improvement.

Source: Journal of Advanced Digital Communications

DOI: 10.53941/jadc.2024.100005

Authors: Adugna Gebrie Jember, Ruiyu Bao, Zijia Yao, Zhao Wang, Zhenyu Zhou, Xiaoyan Wang

Institutions: North China Electric Power University, State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, Ibaraki University

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action, Industry, innovation and infrastructure

Open Access status: hybrid

Funder: China Southern Power Grid