A Novel Ensemble Approach for the Forecasting of Energy Demand Based on the Artificial Bee Colony Algorithm

Autor: Sourav Khanna, Victor Becerra, Adib Allahham, Keiron Roberts
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Mathematical optimization
Control and Optimization
Primary energy
energy demand
ensemble framework
forecasting
machine learning
artificial bee colony algorithm
Energy management
Computer science
020209 energy
Population
Energy Engineering and Power Technology
02 engineering and technology
010501 environmental sciences
01 natural sciences
lcsh:Technology
Gross domestic product
Energy policy
0202 electrical engineering
electronic engineering
information engineering
Electrical and Electronic Engineering
education
Engineering (miscellaneous)
0105 earth and related environmental sciences
Statistical hypothesis testing
education.field_of_study
Ensemble forecasting
Renewable Energy
Sustainability and the Environment
lcsh:T
Artificial bee colony algorithm
Benchmark (computing)
Energy (miscellaneous)
Zdroj: Energies, Vol 13, Iss 3, p 550 (2020)
Energies; Volume 13; Issue 3; Pages: 550
ISSN: 1996-1073
Popis: Accurate forecasting of the energy demand is crucial for the rational formulation of energy policies for energy management. In this paper, a novel ensemble forecasting model based on the artificial bee colony (ABC) algorithm for the energy demand was proposed and adopted. The ensemble model forecasts were based on multiple time variables, such as the gross domestic product (GDP), industrial structure, energy structure, technological innovation, urbanization rate, population, consumer price index, and past energy demand. The model was trained and tested using the primary energy demand data collected in China. Seven base models, including the regression-based model and machine learning models, were utilized and compared to verify the superior performance of the ensemble forecasting model proposed herein. The results revealed that (1) the proposed ensemble model is significantly superior to the benchmark prediction models and the simple average ensemble prediction model just in terms of the forecasting accuracy and hypothesis test, (2) the proposed ensemble approach with the ABC algorithm can be employed as a promising framework for energy demand forecasting in terms of the forecasting accuracy and hypothesis test, and (3) the forecasting results obtained for the future energy demand by the ensemble model revealed that the future energy demand of China will maintain a steady growth trend.
Databáze: OpenAIRE
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