An analysis on the energy consumption of circulating pumps of residential swimming pools for peak load management

Based on an extensive dataset containing aggregated hourly energy consumption readings of residents during March 2011 and October 2012 in South Ontario, Canada, this paper estimates the energy consumption of circulating pumps of residential swimming pools (CPRSP) non-intrusively, and quantifies the impact of CPRSP on the power system. The main challenges are that, first, widely used non-intrusive appliance load monitoring (NIALM) methods are not applicable to this work, due to the low sampling rate and the lack of the energy consumption pattern of CPRSP; second, temperature-based building energy disaggregation methods are not suitable for this work, as they highly depend on the accurate base load estimation and predefined parameters. To overcome these issues, in this paper, first it is found that, during the pool season, for homes with and without swimming pools, the ratio between their base loads is approximately equal to the ratio between their temperature-dependent energy consumptions, then a novel weighted difference change-point (WDCP) model has been proposed. The advantages of the WDCP model are that, on one hand, it doesn’t depend on the base load estimation and predefined parameters; on the other hand, it has no requirement on the data sampling rate and the prior information of energy consumption patterns of CPRSP. Based on the WDCP model it is shown that, the average hourly energy consumption of CPRSP is 0.7425kW, and the minimum and the maximum hourly energy consumptions are 0.5274kW at 9:00 and 0.9612kW at 17:00, respectively. At the peak hour 19:00, July 21, 2011, CPRSP contributes 20.36% energy consumption of homes with swimming pools, as well as 8.48% peak load of all neighborhoods. As a result, the peak load could be reduced by 8.48% if all CPRSP are stopped during the peak hour.

[1]  Hsueh-Hsien Chang,et al.  A New Measurement Method for Power Signatures of Nonintrusive Demand Monitoring and Load Identification , 2011, IEEE Transactions on Industry Applications.

[2]  Kazuya Takeda,et al.  Stochastic modeling and disaggregation of energy-consumption behavior , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[3]  David E. Claridge,et al.  Development of a Toolkit for Calculating Linear, Change–Point Linear and Multiple–Linear Inverse Building Energy Analysis Models, ASHRAE Research Project 1050-RP, Detailed Test Results , 2001 .

[4]  Hsueh-Hsien Chang,et al.  A New Measurement Method for Power Signatures of Nonintrusive Demand Monitoring and Load Identification , 2012 .

[5]  Gang Li.  Sensible heat thermal storage energy and exergy performance evaluations , 2016 .

[6]  Marija D. Ilic,et al.  Smart residential energy scheduling utilizing two stage Mixed Integer Linear Programming , 2015, 2015 North American Power Symposium (NAPS).

[7]  Michael Baldea,et al.  Nonintrusive disaggregation of residential air-conditioning loads from sub-hourly smart meter data , 2014 .

[8]  Dacheng Li,et al.  Load shifting of nuclear power plants using cryogenic energy storage technology , 2014 .

[9]  M. Shin,et al.  Prediction of cooling energy use in buildings using an enthalpy-based cooling degree days method in a hot and humid climate , 2016 .

[10]  Nathan Mendes,et al.  Performance curves of room air conditioners for building energy simulation tools , 2014 .

[11]  Jean Charles Gilbert,et al.  Numerical Optimization: Theoretical and Practical Aspects , 2003 .

[12]  Ian Beausoleil-Morrison,et al.  Disaggregating categories of electrical energy end-use from whole-house hourly data , 2012 .

[13]  Kilian Stoffel,et al.  Change points detection in crime-related time series: An on-line fuzzy approach based on a shape space representation , 2016, Appl. Soft Comput..

[14]  Yu-Hsiu Lin,et al.  Modern development of an Adaptive Non-Intrusive Appliance Load Monitoring system in electricity energy conservation , 2012 .

[15]  Jeff Haberl,et al.  Development of methodology for calibrated simulation in single-family residential buildings using three-parameter change-point regression model , 2015 .

[16]  Seddik Bacha,et al.  Time series distance-based methods for non-intrusive load monitoring in residential buildings , 2015 .

[17]  Rui Sun,et al.  The life cycle rebound effect of air-conditioner consumption in China , 2016 .

[18]  Muhammad Ali Imran,et al.  Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey , 2012, Sensors.

[19]  K. A. Folly,et al.  Overview of non-intrusive load monitoring and identification techniques , 2015 .

[20]  Yang Zhang,et al.  Optimization of a residential district with special consideration on energy and water reliability , 2017 .

[21]  Jinyue Yan,et al.  Potential for carbon sequestration and mitigation of climate change by irrigation of grasslands , 2014 .

[22]  Gang Li.  Energy and exergy performance assessments for latent heat thermal energy storage systems , 2015 .

[23]  Dongdong Li,et al.  A nonintrusive load identification method for residential applications based on quadratic programming , 2016 .

[24]  Pietro Elia Campana,et al.  Dynamic modelling of a pv pumping system with special consideration on water demand , 2013 .

[25]  Eric Ruggieri,et al.  An exact approach to Bayesian sequential change point detection , 2016, Comput. Stat. Data Anal..

[26]  Peng Xu,et al.  HVAC terminal hourly end-use disaggregation in commercial buildings with Fourier series model , 2015 .

[27]  Farhad Kamyab,et al.  Demand Response Program in Smart Grid Using Supply Function Bidding Mechanism , 2016, IEEE Transactions on Smart Grid.

[28]  Peng Hin Lee,et al.  Unsupervised approach for load disaggregation with devices interactions , 2016 .

[29]  Mahmoud-Reza Haghifam,et al.  Load management using multi-agent systems in smart distribution network , 2013, 2013 IEEE Power & Energy Society General Meeting.

[30]  Danny S. Parker.  Research highlights from a large scale residential monitoring study in a hot climate , 2003 .

[31]  Alex Rogers,et al.  An unsupervised training method for non-intrusive appliance load monitoring , 2014, Artif. Intell..

[32]  Saifur Rahman,et al.  A peak-load reduction computing tool sensitive to commercial building environmental preferences , 2016 .

[33]  K. F. Fong,et al.  Potential application of a centralized solar water-heating system for a high-rise residential building in Hong Kong , 2006 .

[34]  Andrew Y. Ng,et al.  Energy Disaggregation via Discriminative Sparse Coding , 2010, NIPS.

[35]  B. Noon,et al.  Using SiZer to detect thresholds in ecological data , 2009 .

[36]  Manish Marwah,et al.  Unsupervised Disaggregation of Low Frequency Power Measurements , 2011, SDM.

[37]  Jinyue Yan,et al.  Model of evapotranspiration and groundwater level based on photovoltaic water pumping system , 2014 .

[38]  Yiyong Cai,et al.  Disaggregating electricity generation technologies in CGE models: A revised technology bundle approach with an application to the U.S. Clean Power Plan , 2015 .

[39]  G.W. Hart,et al.  Residential energy monitoring and computerized surveillance via utility power flows , 1989, IEEE Technology and Society Magazine.

[40]  Yi-Ping Phoebe Chen,et al.  True real time pricing and combined power scheduling of electric appliances in residential energy management system , 2016 .

[41]  J. Frédéric Bonnans,et al.  Numerical Optimization: Theoretical and Practical Aspects (Universitext) , 2006 .

[42]  J. Zico Kolter,et al.  REDD : A Public Data Set for Energy Disaggregation Research , 2011 .

[43]  Hoseong Lee,et al.  Experimental investigation of energy and exergy performance of short term adsorption heat storage for residential application , 2014 .

[44]  J. Renaud.  Numerical Optimization, Theoretical and Practical Aspects— , 2006, IEEE Transactions on Automatic Control.