A review on applications of ANN and SVM for building electrical energy consumption forecasting

• Published in: Renewable & sustainable energy reviews Vol. 33; pp. 102 - 109
• Main Authors: Ahmad, A.S., Hassan, M.Y., Abdullah, M.P., Rahman, H.A., Hussin, F., Abdullah, H., Saidur, R.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01-05-2014; Elsevier
• Subjects: Applied sciences; Artificial Neural Networks; Building energy consumption; Economic data; Electric energy; Energy; Energy economics; Exact sciences and technology; Forecasting; General, economic and professional studies; GMDH; LSSVM; Methodology. Modelling; GMDH; Building energy consumption; Artificial Neural Networks; Forecasting; LSSVM; Energy consumption; Buildings; Electric power consumption; Hybrid model; Support vector machine; Neural network; Artificial intelligence
• ISSN: 1364-0321; 1879-0690
• DOI: 10.1016/j.rser.2014.01.069

The rapid development of human population, buildings and technology application currently has caused electric consumption to grow rapidly. Therefore, efficient energy management and forecasting energy consumption for buildings are important in decision-making for effective energy saving and development in particular places. This paper reviews the building electrical energy forecasting method using artificial intelligence (AI) methods such as support vector machine (SVM) and artificial neural networks (ANN). Both methods are widely used in the field of forecasting and their aim on finding the most accurate approach is ever continuing. Besides the already existing single method of forecasting, the hybridization of the two forecasting methods has the potential to be applied for more accurate results. Further research works are currently ongoing, regarding the potential of hybrid method of Group Method of Data Handling (GMDH) and Least Square Support Vector Machine (LSSVM), or known as GLSSVM, to forecast building electrical energy consumption.