Analysis of Key Factors in Heat Demand Prediction with Neural Networks

Abstract The development of heat metering has promoted the development of statistic models for the prediction of heat demand, due to the large amount of available data, or big data. Weather data have been commonly used as input in such statistic models. In order to understand the impacts of direct solar radiance and wind speed on the model performance comprehensively, a model based on Elman neural networks (ENN) was adopted, of which the results can help heat producers to optimize their production and thus mitigate costs. Compared with the measured heat demand, the introduction of wind speed and direct solar radiation has opposite impacts on the performance of ENN and the inclusion of wind speed can improve the prediction accuracy of ENN. However, ENN cannot benefit from the introduction of both wind speed and direct solar radiation simultaneously.

[1]  Zhang Jian-me.  Prediction of Mid-long Term Load Based on Gray Elman Neural Networks , 2013 .

[2]  Yi Zhang,et al.  Application of a hybrid quantized Elman neural network in short-term load forecasting , 2014 .

[3]  Jianzhou Wang,et al.  Forecasting wind speed using empirical mode decomposition and Elman neural network , 2014, Appl. Soft Comput..

[4]  Wan Fatimah Wan Ahmad,et al.  Function fitting for Control Source Electro-Magnetics data using Elman Neural Network , 2012, 2012 International Conference on Computer & Information Science (ICCIS).

[5]  Yuan Luo,et al.  Host Load Forecasting by Elman Neural Networks , 2012, 2012 International Conference on Control Engineering and Communication Technology.

[6]  Qie Sun,et al.  Statistical analysis of energy consumption patterns on the heat demand of buildings in district heating systems , 2014 .

[7]  Zachary Chase Lipton.  A Critical Review of Recurrent Neural Networks for Sequence Learning , 2015, ArXiv.

[8]  Xiaowei Liu,et al.  Application on stock price prediction of Elman neural networks based on principal component analysis method , 2014, 2014 11th International Computer Conference on Wavelet Actiev Media Technology and Information Processing(ICCWAMTIP).

[9]  Yang Jian.  Short-Term Wind Power Forecasting Based on the Elman Neural Network , 2012 .

[10]  Myles R. Allen,et al.  Cold Extremes in North America vs. Mild Weather in Europe: The Winter of 2013–14 in the Context of a Warming World , 2015 .

[11]  Rui Li,et al.  Thermal Load Prediction Considering Solar Radiation and Weather , 2016 .

[12]  Enric Monte,et al.  “Multiple-input multiple-output vs. single-input single-output neural network forecasting” , 2015 .

[13]  Fredrik Wallin,et al.  A Comprehensive Review of Smart Energy Meters in Intelligent Energy Networks , 2016, IEEE Internet of Things Journal.