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

2017

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.

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