Energy consumption analysis of residential swimming pools for peak load shaving

Abstract Peak load shaving is a very important issue, however, most of peak load shaving methods either require extra investments or reduce the comfort of the consumers. This paper analyzes the impact of circulating pumps of residential swimming pools on the peak load, and shows that shifting the active time of circulating pumps of residential swimming pools could shave the peak load without requiring extra investments or reducing the comfort of the consumers. First, based on an extensive dataset containing hourly energy consumption readings of 1005 residents during March 2011 and October 2012 in South Ontario, this paper analyzes the features of the energy consumptions of residents with and without swimming pools. Second, this paper proposes a novel non-intrusive appliance load monitoring method to estimate the energy consumption of circulating pumps of residential swimming pools. The advantages of the proposed method are that, compared with other non-intrusive appliance load monitoring methods, it does not require high sampling rate data or prior information of the appliance, therefore the cost of implementation is reduced and users’ privacy is protected. Finally, this paper shows that, the average hourly energy consumption of CPRSP is 0.7429 kW h. During the peak hour, circulating pumps of residential swimming pools contributes 20.11% energy consumption of residents with swimming pools, as well as 8.79% peak load of all neighborhoods. When considering minimizing the peak load, by postponing circulating pumps of residential swimming pools for 8 h rather than turn them off, the peak load can be shaved by 4.64%. When considering minimizing the cost of circulating pumps of residential swimming pools, the peak load can be shaved by 3.15% by postponing circulating pumps of residential swimming pools for 6 h, meanwhile the peak hour is postponed from 18:00 to 19:00.

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

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

[3]  Weilin Li,et al.  Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings , 2017 .

[4]  Kuo-Tsang Huang,et al.  Energy consumption characteristics of hotel's marketing preference for guests from regions perspective , 2013 .

[5]  Mark O'Malley,et al.  Capacity value estimation of a load-shifting resource using a coupled building and power system model , 2017 .

[6]  H. Fan,et al.  Statistical analysis of driving factors of residential energy demand in the greater Sydney region, Australia , 2015 .

[7]  Shengwei Wang,et al.  Supply-based feedback control strategy of air-conditioning systems for direct load control of buildings responding to urgent requests of smart grids , 2017 .

[8]  Amin Khodaei,et al.  Probabilistic optimal scheduling of networked microgrids considering time-based demand response programs under uncertainty , 2017 .

[9]  Wenpeng Luan,et al.  Dynamic time warping based non-intrusive load transient identification , 2017 .

[10]  Haiyang Lin,et al.  The energy-saving potential of an office under different pricing mechanisms – Application of an agent-based model , 2017 .

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

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

[13]  Qixin Chen,et al.  Electricity markets evolution with the changing generation mix: An empirical analysis based on China 2050 High Renewable Energy Penetration Roadmap ☆ , 2017 .

[14]  Vincent Lemort,et al.  Residential heat pump as flexible load for direct control service with parametrized duration and rebound effect , 2017 .

[15]  Michael E. Webber,et al.  Modeling electric load and water consumption impacts from an integrated thermal energy and rainwater storage system for residential buildings in Texas , 2017 .

[16]  G. W. Hart,et al.  Nonintrusive appliance load monitoring , 1992, Proc. IEEE.

[17]  Chunhe Song,et al.  An analysis on the energy consumption of circulating pumps of residential swimming pools for peak load management , 2017 .

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

[19]  Ming Zeng,et al.  Impact of behavior-driven demand response on supply adequacy in smart distribution systems , 2017 .

[20]  E. Hobman,et al.  Willingness to participate in direct load control: The role of consumer distrust , 2017 .

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

[22]  Iain MacGill,et al.  Statistical analysis of drivers of residential peak electricity demand , 2017 .

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

[24]  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 .

[25]  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.

[26]  Mark Gillott,et al.  Optimum community energy storage for renewable energy and demand load management , 2017 .

[27]  Dirk Saelens,et al.  Generic characterization method for energy flexibility: Applied to structural thermal storage in residential buildings , 2017 .

[28]  Yong Wang,et al.  Real-time electricity pricing for industrial customers: Survey and case studies in the United States , 2017 .

[29]  Andrea Castelletti,et al.  A Hybrid Signature-based Iterative Disaggregation algorithm for Non-Intrusive Load Monitoring , 2017 .

[30]  Jing Liao,et al.  Measuring the energy intensity of domestic activities from smart meter data , 2016 .

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

[32]  Stefano Cordiner,et al.  Fuel cell based Hybrid Renewable Energy Systems for off-grid telecom stations: Data analysis from on field demonstration tests , 2017 .

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

[34]  Carey W. King,et al.  The energy-water nexus in Texas , 2011 .

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

[36]  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 .