Abstract
To enhance the prediction performance for building energy consumption, this paper presents a modified deep belief network (DBN) based hybrid model. The proposed hybrid model combines the outputs from the DBN model with the energy-consuming pattern to yield the final prediction results. The energy-consuming pattern in this study represents the periodicity property of building energy consumption and can be extracted from the observed historical energy consumption data. The residual data generated by removing the energy-consuming pattern from the original data are utilized to train the modified DBN model. The training of the modified DBN includes two steps, the first one of which adopts the contrastive divergence (CD) algorithm to optimize the hidden parameters in a pre-train way, while the second one determines the output weighting vector by the least squares method. The proposed hybrid model is applied to two kinds of building energy consumption data sets that have different energy-consuming patterns (daily-periodicity and weekly-periodicity). In order to examine the advantages of the proposed model, four popular artificial intelligence methods—the backward propagation neural network (BPNN), the generalized radial basis function neural network (GRBFNN), the extreme learning machine (ELM), and the support vector regressor (SVR) are chosen as the comparative approaches. Experimental results demonstrate that the proposed DBN based hybrid model has the best performance compared with the comparative techniques. Another thing to be mentioned is that all the predictors constructed by utilizing the energy-consuming patterns perform better than those designed only by the original data. This verifies the usefulness of the incorporation of the energy-consuming patterns. The proposed approach can also be extended and applied to some other similar prediction problems that have periodicity patterns, e.g., the traffic flow forecasting and the electricity consumption prediction.
Highlights
With the growth of population and the development of economy, more and more energy is consumed in the residential and office buildings
In order to examine the superiority of the hybrid model for the office building energy consumption prediction, the five prediction models are compared under the consideration of different data types
Smaller values of the mean absolute error (MAE), root mean square error (RMSE) and mean relative error (MRE) represent better prediction results while lager values of r and R2 correspond to better performance
Summary
With the growth of population and the development of economy, more and more energy is consumed in the residential and office buildings. Numerous data-driven artificial intelligence approaches have been proposed for building energy consumption prediction. The RBM is an undirected, bipartite graphical model, which consists of the visible (input) layer and the hidden (output) layer. To make a quantitative assessment of the proposed MDBN based hybrid model, four popular artificial intelligence approaches, the BPNN, GRBFNN, ELM, and SVR, are chosen as the comparative approaches and introduced briefly below. Hidden layer wij wij wiL1+1 xn n n2 nL l=0 l=1 l=2 l =L. the final output of the network can be expressed as [40,41,42]
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