Building load management using cluster and statistical analyses

Abstract Building energy management systems has become very significant to monitor and control loads, mainly due to the tariff and demand side management programs. This technology allows the performance of key energy management functions such as organizing energy use data, identifying energy consumption anomalies, managing energy costs, and automating demand response strategies. Load management techniques allow the energy manager to reshape the building load curve and reduce the peak demand. The synthesis of load shapes is one of the most critical steps in evaluating load management programs. The energy manager should be able to recognize the typical shape of the daily load curve in order to manage the energy use. This paper presents a methodology to perform the synthesis of load shapes. On that purpose, it is essential to identify typical daily load curves as well as the best way to find their representative model. By getting to know the typical load curves of a building, an energy manager may predict load and peak demands. Results have shown the efficiency of clustering technology in the analysis of time series data such as load curves. Using historical data, the total accumulated energy in the end of the day as well as the maximum peak demand of the day may be predicted.

[1]  G. Michalik,et al.  Modelling of energy use patterns in the residential sector using linguistic variables , 1996, Proceedings of International Conference on Intelligent System Application to Power Systems.

[2]  Jin Yang,et al.  On-line building energy prediction using adaptive artificial neural networks , 2005 .

[3]  Carlos E. Pedreira,et al.  Neural networks for short-term load forecasting: a review and evaluation , 2001 .

[4]  Jian Wang,et al.  Short, medium and long term load forecasting model and virtual load forecaster based on radial basis function neural networks , 2010 .

[5]  J. A. Foosnaes,et al.  Estimation of annual coincident peak demand and load curves based on statistical analysis and typical load data , 1993 .

[6]  D. Claridge,et al.  A Fourier Series Model to Predict Hourly Heating and Cooling Energy Use in Commercial Buildings With Outdoor Temperature as the Only Weather Variable , 1999 .

[7]  Robert J. Marks,et al.  Electric load forecasting using an artificial neural network , 1991 .

[8]  Tharam S. Dillon,et al.  Short term load forecasting using an adaptive neural network , 1991 .

[9]  R. E. Abdel-Aal,et al.  Modeling and forecasting electric daily peak loads using abductive networks , 2006 .

[10]  V. Geros,et al.  Modeling and predicting building's energy use with artificial neural networks: Methods and results , 2006 .

[11]  Ramazan Bayindir,et al.  Development of a real time energy monitoring platform , 2011 .

[12]  Z. Styczynski,et al.  Application of statistical and neural approaches to the daily load profiles modelling in power distribution systems , 1999, 1999 IEEE Transmission and Distribution Conference (Cat. No. 99CH36333).

[13]  John E. Seem,et al.  Pattern recognition algorithm for determining days of the week with similar energy consumption profiles , 2005 .

[14]  Minho Kim,et al.  New indices for cluster validity assessment , 2005, Pattern Recognit. Lett..

[15]  Ismet Erkmen,et al.  Intelligent short-term load forecasting in Turkey , 2006 .

[16]  Deepa Singhal,et al.  Electricity price forecasting using artificial neural networks , 2011 .

[17]  F. Kianifard.  Applied Multivariate Data Analysis: Volume II: Categorical and Multivariate Methods , 1994 .

[18]  Keith Baker,et al.  Improving the prediction of UK domestic energy-demand using annual consumption-data , 2008 .

[19]  Tomonobu Senjyu,et al.  A neural network based several-hour-ahead electric load forecasting using similar days approach , 2006 .

[20]  Silja Meyer-Nieberg,et al.  Electric load forecasting methods: Tools for decision making , 2009, Eur. J. Oper. Res..

[21]  C.E.P D. Victor,et al.  Effective Load Management Planning—A Case Study , 2002 .

[22]  Junichi Murata,et al.  Daily load curve forecasting via automatic selection of models , 1991 .

[23]  J. Hair.  Multivariate data analysis , 1972 .

[24]  Pei-Chann Chang,et al.  Monthly electricity demand forecasting based on a weighted evolving fuzzy neural network approach , 2011 .