Abstract
With growing urbanization, it has become necessary to manage this growth smartly. Specifically, increased electrical energy consumption has become a rapid urbanization trend in China. A building model based on a neural network was proposed to overcome the difficulties of analytical modelling. However, increased amounts of data, repetitive computation, and training time become a limitation of this approach. A simplified model can be used instead of the full order model if the performance is acceptable. In order to select effective data, Mean Impact Value (MIV) has been applied to select meaningful data. To verify this neural network method, we used real electricity consumption data of a shopping mall in China as a case study. In this paper, a Bayesian Regularization Neural Network (BRNN) is utilized to avoid overfitting due to the small amount of data. With the simplified data set, the building model showed reasonable performance. The mean of Root Mean Square Error achieved is around 10% with respect to the actual consumption and the standard deviation is low, which reflects the model’s reliability. We also compare the results with our previous approach using the Levenberg–Marquardt back propagation (LM-BP) method. The main difference is the output reliability of the two methods. LM-BP shows higher error than BRNN due to overfitting. BRNN shows reliable prediction results when the simplified neural network model is applied.
Highlights
China’s total electricity consumption in 2015 was 5.55 trillion kWh, and this will increase drastically with China’s economic growth
We propose a novel simplified neural network with the help of mean impact value (MIV) to predict building electricity consumption and to improve the prediction accuracy of building electricity consumption
Actual data of building electricity consumption has been obtained from a shopping mall in Dalian, China
Summary
China’s total electricity consumption in 2015 was 5.55 trillion kWh, and this will increase drastically with China’s economic growth. In order to derive a building electricity consumption forecasting model, we use analytical and heuristic modelling These methods implement a mathematical model and a neural network, respectively. Zeng and Yeung proposed a sensitivity analysis by combining the above two approaches, and derived output variance caused by the perturbation of input and weight. They pointed out that their result can help to select more weight sets with low sensitivity level during the training [24]. Changed output value indicates the influence of input variation on output change, and we can notice that the input data has an important influence on the change of the output variation This result gives a clue relating to organizing the simplifying the neural network model to avoid unnecessary input. Analysis the relation input combination and output carried out
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