Title: Forecasting electrical consumption of commercial buildings using energy performance indicators

Authors: Evangelos Spiliotis; Axilleas Raptis; Zampeta Nikoletta Legaki; Vassilios Assimakopoulos

Addresses: Forecasting and Strategy Unit, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou Str, 15773 Zografou Athens, Greece ' Forecasting and Strategy Unit, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou Str, 15773 Zografou Athens, Greece ' Forecasting and Strategy Unit, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou Str, 15773 Zografou Athens, Greece ' Forecasting and Strategy Unit, School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou Str, 15773 Zografou Athens, Greece

Abstract: This paper presents a methodology for predicting electrical consumption in energy-intensive commercial buildings through a range of energy performance indicators. Specifically, the most representative indicators per energy end use of the building (lighting, kitchen, refrigerators, etc.) are defined and appropriate time series forecasting methods are applied at its time series to predict future energy performance of the whole building. In order to determine the accuracy of the methodology, a fast-food restaurant in Cyprus was selected and the electrical consumption was measured over a winter month. Then, the methodology was applied for multiple forecasting horizons and electrical consumption predictions were compared with the actual consumption. The results indicated a relatively satisfactory performance of the methodology for all the horizons tested, revealing at the same time that the length of the forecasting period has a significant impact on the accuracy of the predicted energy consumption of buildings.

Keywords: energy performance indicators; EPIs; energy forecasting; energy saving techniques; energy assessment; commercial buildings; time series forecasting; fast-food restaurants; Cyprus.

DOI: 10.1504/IJDSS.2015.067556

International Journal of Decision Support Systems, 2015 Vol.1 No.2, pp.164 - 182

Received: 27 Feb 2014
Accepted: 13 May 2014
Published online: 18 Mar 2015 *

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