An evolutionary-based predictive soft computing model for the prediction of electricity consumption using multi expression programming

• Main Authors: Fallahpour, Alireza, Wong, Kuan Yew, Rajoo, Srithar, Tian, Guangdong
• Format: Article
• Published: Elsevier Ltd 2021
• Subjects: TJ Mechanical engineering and machinery
• Online Access: http://eprints.utm.my/id/eprint/94930/; http://dx.doi.org/10.1016/j.jclepro.2020.125287
• Institution: Universiti Teknologi Malaysia

Proper estimation of electricity consumption is one of the influential factors for sustainability and cleaner production in both developed and developing countries. Many studies have been conducted to present accurate prediction models for forecasting electricity demand. However, researchers are still working to develop models with higher accuracy. This study applies a newer branch of Genetic Programming (GP) as a soft computing technique, known as Multi Expression Programming (MEP) to predict the electricity consumption of China for the first time based on the data collected from 1991 to 2019. Specifically, a robust mathematical model was developed using MEP for this purpose. Different predictive techniques known as Gene Expression Programming (GEP) and Adaptive Neuro Fuzzy Inference System (ANFIS) were used to compare the accuracy of the model. Based on the results, the proposed MEP model is more powerful and accurate than both GEP and ANFIS. In addition, a sensitivity analysis was conducted to present the impact of each factor on the electricity consumption of China. It was shown that among the four independent factors (Population, Gross Domestic Product (GDP), Import, and Export), Population has the highest impact, followed by Export, Import and GDP, respectively.