An SVR-based Building-level Load Forecasting Method Considering Impact of HVAC Set Points

This paper focuses on a technique to determine the impact of HVAC set point adjustments on building-level electrical load (kW) utilizing Support Vector Regression (SVR) with the minimum possible set of input variables. The paper uses two SVR-based forecasting methods, namely single-step and recursiv...

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Bibliographic Details
Published in2019 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) pp. 1 - 5
Main Authors Haque, Ashraful, Pipattanasomporn, Manisa, Rahman, Saifur, Kothandaraman, Raghavan, Malekpour, Ahmadreza, Paaso, Esa Aleksi, Bahramirad, Shay
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.02.2019
Subjects
demand response
load forecasting
machine learning
regression model
SVR
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Summary:This paper focuses on a technique to determine the impact of HVAC set point adjustments on building-level electrical load (kW) utilizing Support Vector Regression (SVR) with the minimum possible set of input variables. The paper uses two SVR-based forecasting methods, namely single-step and recursive models. These models are used to forecast hourly electrical loads of a commercial building in Chicago area for the summer period from 8AM to 8PM. The model accuracy is observed to be higher than 95% for hour-ahead load forecasts, and higher than 93% for 12-hour ahead load forecasts. The models presented in the paper can be used to quantify the reduction in electrical load (kW) based on HVAC set point adjustments during peak hours in buildings.
ISSN:2472-8152
DOI:10.1109/ISGT.2019.8791649