Modeling the cost of energy in public sector buildings by linear regression and deep learning
Modeling the cost of energy consumption of public buildings is vital for planning reconstruction measures in the public sector. The methods of predictive analytics have not been sufficiently exploited in this domain. This paper aimed to create a model for predicting the cost of the total energy consumption of a building based on deep learning (DL) and compare it to the standard linear regression (MLR), as well to identify key predictors that can significantly influence the cost of energy. An algorithm for modeling procedure is proposed which includes data pre-processing, variable reduction procedures, training and testing MLR and deep neural networks (DNN) and, finally, performance evaluation. Variable reduction in the MLR model was conducted by a backward procedure; while in DNN, the Olden method was used. The algorithm was tested on a high-dimensional real dataset of Croatian public buildings. The results showed that there is a statistically significant difference in the distribution of DNN predictions and distribution of actual values in the validation set, as opposed to distribution of MLR predictions and real values. However, DNN model had a lower normalized root mean square error, while the MLR model had a lower symmetric mean average error. Those findings reveal the potential of DL for solving this type of problems but also the need for more advanced algorithms adjusted to deal with large-range numeric outputs. The created models could be implemented in public sector business intelligence systems to support policy and decision makers in allocating resources for building reconstructions.
KeywordsEnergy cost Data analytics Deep learning Multiple linear regression Public sector buildings
This work was supported by Croatian Science Foundation through research Grant IP-2016-06-8350 “Methodological framework for efficient energy management by intelligent data analytics” MERIDA and research Grant IP-2016-06-6545 “The optimization and statistical models and methods in recognizing properties of data sets measured with errors”.
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