توسعه مدل های پیش بینی برای مصرف انرژی ساختمان در روز بعد، و اوج تقاضای برق با استفاده از تکنیک های داده کاوی

The approach is adopted to analyze the large energy consumption data of the tallest building in Hong Kong. The prediction accuracies of the ensemble models measured by mean absolute percentage error (MAPE) are 2.32% and 2.85% for the next-day energy consumption and peak power demand respectively, which are evidently higher than those of individual base models. The results also show that the outlier detection method is effective in identifying the abnormal daily energy consumption profiles. The RFE process can significantly reduce the computation load while enhancing the model performance. The ensemble models are valuable for developing strategies of fault detection and diagnosis, operation optimization and interactions between buildings and smart grid.