Benchmarking performance of machine-learning methods for building energy demand modelling
Küçük Resim Yok
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ice Publishing
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The relevance, relative importance and co-linearity of input parameters to the results of building energy demand forecasts were investigated. Two calendar years of historical data including weather variables and days of week were used. The study also aimed to assess the performance of multiple-linear-regression, support-vector-machine and artificial-neural-network models for predicting daily heating, ventilation and air-conditioning energy consumption of a commercial building in France. Mean absolute error, root mean square error and coefficient of variation of root-mean squared error were selected as the performance criteria. The results showed that the best performance was achieved via the artificial-neural-network model according to all performance measures. In addition, the other two models were not able to meet the predicting requirements for energy consumption in a building since their coefficient-of-variation-o-root-mean-squared error values were not below 30%. The results also indicated that there was multiple co-linearity between the number of degree days and outdoor temperature. Furthermore, the most significant parameter on daily energy consumption was found to be the number of degree days, followed by global radiation, sunshine rate and the day of the week, respectively.
Açıklama
Anahtar Kelimeler
Artificial Neural-Networks, Support Vector Regression, Cooling Load Prediction, Electricity Consumption, Intelligence, Simulation, Forecast, Weather
Kaynak
Proceedings Of The Institution Of Civil Engineers-Engineering Sustainability
WoS Q Değeri
Q4
Scopus Q Değeri
Q3
