A review on applications of ANN and SVM for building electrical energy consumption forecasting
| Autor: | M. P. Abdullah, Hasimah Abdul Rahman, Huda Abdullah, Ahmad Sukri Ahmad, Rahman Saidur, Faridah Hussin, Mohammad Yusri Hassan |
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| Rok vydání: | 2014 |
| Předmět: |
education.field_of_study
Engineering Artificial neural network Renewable Energy Sustainability and the Environment Group method of data handling business.industry Electric potential energy Population Energy consumption Machine learning computer.software_genre Field (computer science) Support vector machine Artificial intelligence education business computer Efficient energy use |
| Zdroj: | Renewable and Sustainable Energy Reviews. 33:102-109 |
| ISSN: | 1364-0321 |
| DOI: | 10.1016/j.rser.2014.01.069 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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