Authors:
Clement Lork
1
;
Yuren Zhou
1
;
Rajasekhar Batchu
2
;
Chau Yuen
1
and
Naran M. Pindoriya
2
Affiliations:
1
Singapore University of Technology and Design, Singapore
;
2
Indian Institute of Technology Gandhinagar, India
Keyword(s):
Residential AC Modelling, Data Driven, Forecasting, Regression Trees, Feature Selection, Machine Learning.
Related
Ontology
Subjects/Areas/Topics:
Energy and Economy
;
Energy Management Systems (EMS)
;
Energy Monitoring
;
Energy Profiling and Measurement
;
Energy-Aware Systems and Technologies
;
Evolutionary Algorithms in Energy Applications
Abstract:
Residential Air Conditioning (AC) load has a huge role to play in Demand Response (DR) Programs as it is one of the power intensive and interruptible load in a home. Due to the variety of ACs types and the different sizes of residences, modelling the power consumption of AC load individually is non-trivial. Here, an adaptive framework based on Regression Trees is proposed to model and forecast the power consumption of different AC units in different environments by taking in just 6 basic variables. The framework consists of an automatic feature selection process, a load prediction module, an indoor temperature forecasting module, and is capped off by a load forecasting module. The effectiveness of the proposed approach is evaluated using data set from an ongoing research project on air-conditioning system control for energy management in a residential test bed in Singapore. Experiments on highly dynamic loads gave a maximum Mean Absolute Percentage Error (MAPE) of 21.35% for 30min ah
ead forecasting and 27.96% for day ahead forecasting.
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