NDLI: Energy storage sizing for office buildings based on short-term load forecasting

Content Provider	IEEE Xplore Digital Library
Author	Xiaohui Yan Haisheng Chen Xuehui Zhang Chunqing Tan
Copyright Year	2012
Description	Author affiliation: Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing, China, 0086-100190 (Xiaohui Yan; Haisheng Chen; Xuehui Zhang; Chunqing Tan)
Abstract	This paper presents a three-layer Artificial Neural Network as the short-term load forecasting model adopting the fastest back-propagation algorithm with robustness, i.e., Levenberg-Marquardt optimization, and moreover, the momentum factor is considered during the learning process. Based on predicted data by aforementioned model, size determination of energy storage system in terms of power rating and capacity is undertaken according to the desired level of shaving peak demand. The illustrative example in reference to the weather and power load data of office building from July to August in 2011 gets the results that the average relative error −0.7% and the root-mean-square error 2.79% which show aforementioned forecasting model can work effectively with the attractive percentage, i.e. 87.5%, of error within the acceptable one 2.79%; Furthermore, size determination of energy storage system adopting battery energy storage technology, i.e. 7.03kW/36.42kWh, is carried out to meet the desired peak shaving demand.
Starting Page	290
Ending Page	295
File Size	172075
Page Count	6
File Format	PDF
ISBN	9781467319768
e-ISBN	9781467319751
DOI	10.1109/ICIAFS.2012.6419919
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-09-27
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Load forecasting Buildings Artificial neural networks Predictive models Forecasting Load modeling Energy storage
Content Type	Text
Resource Type	Article

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