Polynomial-Fourier series model for analyzing and predicting electricity consumption in buildings

Significance Statement

Water and electricity are two essential elements that need to be used effectively and efficiently and should not be wasted. Various methods have been adopted to manage the amount of energy being used everywhere. Accurate prediction of energy consumption in buildings is considered in this research paper. The electrical usages in buildings vary depending on the activity of the user.

Global climate change or ageing of equipment are some factors that affect energy consumption. The construction models find it difficult to accurately predict the consumption of electricity in buildings as energy consumption varies in terms of periodic and irregular activities.

To estimate the electricity consumption and its performance in buildings, DOE-2 based building energy simulation program has been proposed by this team following the earlier researchers. For accessing a building’s thermal response, sensitivity techniques have to be used. This helped energy professionals to detect changes in electrical consumption rapidly. To predict regional energy consumption Artificial Neural Network (ANN) model was proposed which provided accurate forecasts. They also used applied Adaptive Network based Fuzzy Interference System (ANFIS). This also provided them accurate forecasts.

Interestingly Fourier series model was used to analyze structural vibration and to investigate the periodic demand of electric demand series which provided only satisfactory results. To characterize regular electricity demand, Gaussian processes are found to be useful and to predict uniform electricity consumption Fourier transforms is better suited.

Researchers led by Professor Chin-Shiang Chang at National Yunlin University of Science & Technology, Taiwan proposed another application to analyze electricity consumption, a combination of polynomial and Fourier series known as Polynomial Fourier series is developed (P-FS). Here consumption is mostly determined by periodical activities and long term trends (eg.  university libraries). Initially, raw data is collected for analysis. The new findings appear in the journal, Energy and Buildings.

In their study the data set was separated into two parts; one to determine the parameters of the model and the other to determine the number of terms for the polynomial. The real data is compared with the predictions based on Polynomial Fourier series. If more terms are selected in the polynomial the better will be the fitting that appears for the first part of analyzed data. Errors are taken and expressed as Fourier series of sinusoid. The sinusoid term with maximum amplitude is picked but the condition is that it should fit with the data of the second part. The more the sinusoidal terms better will be the fitting. The sinusoids represent the consumption of electricity due to different usage levels.

The main advantage of the Polynomial Fourier Series proposed in this study is better evaluation of energy policy and accurate predictions of electricity consumption even with limited data compared to other models.

Polynomial-Fourier series model for analyzing and predicting electricity consumption in buildings - Advances in Engineering

Fig. 1. Monthly data of electricity consumption of the library of Yuntech from January 2009 to December 2013.

Polynomial-Fourier series model for analyzing and predicting electricity consumption in buildings - Advances in Engineering

Fig. 2. Bi-monthly data of electricity consumption of an office building in Yunlin, Taiwan from February 2008 to November 2011.

Polynomial-Fourier series model for analyzing and predicting electricity consumption in buildings - Advances in Engineering

Fig. 3. Bi-monthly data of electricity consumption of a civilian house in Yunlin, Taiwan from February 2010 to November 2013.

About The Author

Cho-Liang Tsai is a Professor of Department of Civil and Construction Engineering, since 1996, and also Dean of Student Affairs, since 2013, of National Yunlin University of Science & Technology, Yunlin 64002, Taiwan.

Prof. Tsai obtained his Bachelor degree from Civil Engineering Department, National Cheng-Kung University in Taiwan, 1982, Master degree of Civil Engineering Department, Northwestern University in Ill. U.S.A., 1987 and PhD of Department of Theoretical and Applied Mechanics, Northwestern University in Ill. U.S.A., 1991. Prof. Tsai majors in construction materials, theoretical mechanics and experimental mechanics.

About The Author

Wei Tong Chen received his PhD at the Department of Civil Engineering at University of Florida, Dr. Wei Tong Chen is a Professor and the Chairperson of the Department of Civil and Construction Engineering at National Yunlin University of Science and Technology (YunTech) in Taiwan. He has been the chief editor of Value Management Journal for 6 year. Currently, he is the chief editor of Journal of Property Management.

Professor Chen also serves the publishing committee of the Construction Management Association of Taiwan. He is now principally involved in innovative project performance measurement, construction management, construction safety, energy saving, property management, and value engineering application with an active interest in research. He published more than 130 papers in construction management and property management in last 10 years.

About The Author

Chin-Shiang Chang is a research assistant of Department of Civil and Construction Engineering, National Yunlin University of Science & Technology, Yunlin 64002, Taiwan. He majors in construction management with the specialty of energy usage prediction.

Journal Reference

Cho-Liang Tsai, Wei Tong Chen, Chin-Shiang Chang, Polynomial-Fourier series model for analyzing and predicting electricity consumption in buildings, Energy and Buildings, Volume 127,  2016, Pages 301–312.

Dept. of Construction Engineering, National Yunlin University of Science & Technology No. 123, University Road, Section III, Douliu, Yunlin 64002, Taiwan

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