Deployment of data-mining short and medium-term horizon cooling load forecasting models for building energy optimization and management

In this study, data-mining techniques comprising three forecasting algorithms for accurate and precise cooling load requirement prediction in the building environment, with the primary aim and the objective of improving the load management are applied. Three state-of-the-art cooling load prediction algorithms are - multiple-linear regression (MLR) model, Gaussian process regression (GPR) model and Levenberg-Marquardt backpropagation neural network (LMB-NN) model. The Pearson correlation analysis is practiced calculating the correlation between actual cooling load demand and input feature variables of climate parameters. The impact of climate variability on the building load requirement is also analyzed. Forecasting intervals are divided into two basic parts: (i) 7-day ahead prediction; and (ii) 1-month ahead prediction. To assess the prediction performance, four performance evaluation indices are applied, which are: (i) coefficient of correlation (R); (ii) mean absolute error (MAE); (iii) mean absolute percentage error (MAPE); and (iv) coefficient of variation (CV). The model's performance is compared with the selection of different hidden neurons at different load conditions. The MAPE for 7-day ahead prediction interval by MLR, GPR and LMB-NN model is 13.053%, 0.405% and 2.592%, respectively. Furthermore, the data-mining algorithms are compared and validated with the previous study, and the MAPE of Bayesian regularization neural networks is calculated 2.515% for 7-day ahead prediction. It was witnessed that the algorithms could be applied to facilitate the building cooling load prediction, by applying a relatively limited number of parameters related to energy usage as well as environmental impact in the building environment. The forecasting results show that the three algorithms are effective in predicting the irregular behavior in the data as well as cooling load demand prediction. (C) 2018 Elsevier Ltd and IIR. All rights reserved.