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Title |
Smart buildings Cooling and Heating Load Forecasting Models: Review |
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Author |
Mohammed Bakri Bashir and Abdullah Alhumaidi Alotaibi |
| Citation |
Vol. 20 No. 12 pp. 79-94 |
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Abstract |
The proper implementation of the building cooling and heating load prediction models is a key factor to enhance the energy efficiency buildings usage. In the recent years, a number of researches conducted to forecast the cooling and heating loads. Most challenging and significant parts of prediction are determining the most efficient input parameters and develop a high accuracy prediction models. Several data-driven prediction models are proposed to develop the best control of the energy consumption systems, while provide a suitable indoor comfort environment. Despite of the number of review articles discussed the advantage and disadvantages of prediction models, there are gaps in reviewing cooling and heating load prediction models. This study provides a critical review of recent models used in cooling and heating load prediction by focusing on model performance and accuracy. The comparative analysis of the review shows that each prediction model has particular advantages in comparison to other models. Additionally, the review comes out with that most of the models has shortcomings from the parameters considered as input, and the techniques used to implement the models. The aim of this review is to highlight the disadvantages of existing models used in the cooling and heating load predictions and provide a comparative analysis of these techniques. |
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Keywords |
Cooling load prediction, heating load prediction , HVAC systems, Artificial Neural network, Support vector machine, Deep learning, regression analaysis |
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