A comparative analysis of building energy estimation methods in the context of demand response

Abstract A critical element of assessing a building's suitability for Demand Side Response (DSR) is understanding its turndown potential to ensure that DSR participation will be financially viable. While research has been undertaken on site level DSR estimation methods, there is currently no research that compares the outcomes of these methods. This paper compares four non-domestic energy estimation methods used for understanding the DSR potential of electrical appliances in a building to provide insights about uncertainty levels based on input requirements. Each method is deployed to estimate the DSR potential of HVAC chiller assets at two UK hotels over two years. The results show the methods have a range of error levels from the highest Mean Average Percentage Error (MAPE) of 159% to the lowest MAPE of 39%. The input requirements followed a general trend of more complex informational inputs resulting in lower error values. The outcomes of this research enable users to make informed decisions in selecting DSR estimation methods based on information availability and acceptable estimation error levels.

[1]  Rob J Hyndman,et al.  25 years of time series forecasting , 2006 .

[2]  David J. Ketchen,et al.  THE APPLICATION OF CLUSTER ANALYSIS IN STRATEGIC MANAGEMENT RESEARCH: AN ANALYSIS AND CRITIQUE , 1996 .

[3]  Jacopo Torriti,et al.  Demand side flexibility and responsiveness: moving demand in time through technology , 2018 .

[4]  Johanna L. Mathieu,et al.  Characterizing the Response of Commercial and Industrial Facilities to Dynamic Pricing Signals From the Utility , 2010 .

[5]  Johanna L. Mathieu,et al.  Quantifying Changes in Building Electricity Use, With Application to Demand Response , 2011, IEEE Transactions on Smart Grid.

[6]  Ecmwf Newsletter,et al.  EUROPEAN CENTRE FOR MEDIUM-RANGE WEATHER FORECASTS , 2004 .

[7]  Mitchell Curtis,et al.  Demand side response aggregators: How they decide customer suitability , 2017, 2017 14th International Conference on the European Energy Market (EEM).

[8]  Zhengwei Li,et al.  Performance evaluation of conventional demand response at building-group-level under different electricity pricings , 2016 .

[9]  Theofilos A. Papadopoulos,et al.  Pattern recognition algorithms for electricity load curve analysis of buildings , 2014 .

[10]  Jlm Jan Hensen,et al.  Evaluating energy performance in non-domestic buildings : a review , 2016 .

[11]  David P. Chassin,et al.  Aggregate modeling of fast-acting demand response and control under real-time pricing , 2016 .

[12]  Dino Bouchlaghem,et al.  Predicted vs. actual energy performance of non-domestic buildings: Using post-occupancy evaluation data to reduce the performance gap , 2012 .

[13]  Yogesh L. Simmhan,et al.  Prediction models for dynamic demand response: Requirements, challenges, and insights , 2015, 2015 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[14]  Jarke J. van Wijk,et al.  Cluster and Calendar Based Visualization of Time Series Data , 1999, INFOVIS.

[15]  Xin Jin,et al.  K-Means Clustering , 2010, Encyclopedia of Machine Learning.

[16]  Junqiao Han Comparison of Demand Response Performance with an EnergyPlus Model in a Low Energy Campus Building , 2010 .

[17]  Pedro Paulo Leite do Prado,et al.  Pattern recognition algorithms , 2008 .

[18]  Yeonsook Heo,et al.  Sensitivity analysis methods for building energy models: Comparing computational costs and extractable information , 2016 .

[19]  Rongxin Yin,et al.  IMPROVEMENT OF DEMAND RESPONSE QUICK ASSESSMENT TOOL (DRQAT) AND TOOL VALIDATION CASE STUDIES , 2015 .