Abstract
A hybrid method which combines time series model and artificial intelligence method is proposed in this paper to improve the prediction accuracy of building thermal load. Firstly, a simple auto regressive (AR) model is utilized to predict present load using previous loads, the order and the parameters of AR model are identified by the data produced by DeST. Then, a 3-layer back-propagation neural network optimized by particle swarm optimization (PSO) neural network (PNN) is set up to predict the error which is derived by comparing the precious AR predicting load. The error and its corresponding meteorological data generate the training sample data. At last, the hybrid model, named autoregressive and particle swarm neural network (APNN), is obtained. It uses historical load information and real-time meteorological data as input to predict a refined real-time load by adding error to preparative load. To evaluate the prediction accuracy, this hybrid model APNN is compared with several common methods via different statistical indicators, the result show the APNN hybrid method has higher accuracy in thermal load forecasting.
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Acknowledgment
Thanks to the supports by National Natural Science Foundation (NNSF) of China under Grant 61273190 and Shanghai Natural Science Foundation under Grant 13ZR1417000.
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© 2016 Springer Science+Business Media Singapore
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Liu, T., Liu, K., Fang, P., Zhao, J. (2016). A Hybrid Model of AR and PNN Method for Building Thermal Load Forecasting. In: Zhang, L., Song, X., Wu, Y. (eds) Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems. AsiaSim SCS AutumnSim 2016 2016. Communications in Computer and Information Science, vol 643. Springer, Singapore. https://doi.org/10.1007/978-981-10-2663-8_16
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DOI: https://doi.org/10.1007/978-981-10-2663-8_16
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