An Adaptive Data Driven Approach to Single Unit Residential Air-conditioning Prediction and Forecasting using Regression Trees

Clement Lork, Yuren Zhou, Rajasekhar Batchu, Chau Yuen, Naran M. Pindoriya

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 ahead forecasting and 27.96% for day ahead forecasting.

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Paper Citation


in Harvard Style

Lork C., Zhou Y., Batchu R., Yuen C. and Pindoriya N. (2017). An Adaptive Data Driven Approach to Single Unit Residential Air-conditioning Prediction and Forecasting using Regression Trees . In Proceedings of the 6th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS, ISBN 978-989-758-241-7, pages 67-76. DOI: 10.5220/0006309500670076


in Bibtex Style

@conference{smartgreens17,
author={Clement Lork and Yuren Zhou and Rajasekhar Batchu and Chau Yuen and Naran M. Pindoriya},
title={An Adaptive Data Driven Approach to Single Unit Residential Air-conditioning Prediction and Forecasting using Regression Trees},
booktitle={Proceedings of the 6th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,},
year={2017},
pages={67-76},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006309500670076},
isbn={978-989-758-241-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,
TI - An Adaptive Data Driven Approach to Single Unit Residential Air-conditioning Prediction and Forecasting using Regression Trees
SN - 978-989-758-241-7
AU - Lork C.
AU - Zhou Y.
AU - Batchu R.
AU - Yuen C.
AU - Pindoriya N.
PY - 2017
SP - 67
EP - 76
DO - 10.5220/0006309500670076