Forecasting Electricity Consumption of Appliances based on the Additive Factor Hidden Markov Model
Q. Gao, Y. Su, L. B. Wu, Y. Zhou
2018
Abstract
Accurately forecasting the electricity demand and feedback on separate appliances can lead to natural energy-saving behaviors and higher energy efficiency. In this paper, a supervised additive factor hidden Markov model including the exogenic influencing factors is developed to forecast the electricity usage of appliance in building based on the hourly data, where the model is trained with the first part of appliances data to get optimized parameters and tuned with aggregate electricity data in forecasting. The model successfully forecast the usage of all appliances for a mall building and the accuracies for appliances are all larger than 60%, which is higher than most similar analyses, especially same frequency data analysis. By adding the influence factors, the AFHMM model gives a more accurate result in lower frequency data, which can be widely used in energy monitoring of buildings.
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in Harvard Style
Gao Q., Su Y., Wu L. and Zhou Y. (2018). Forecasting Electricity Consumption of Appliances based on the Additive Factor Hidden Markov Model.In Proceedings of the International Workshop on Environmental Management, Science and Engineering - Volume 1: IWEMSE, ISBN 978-989-758-344-5, pages 193-200. DOI: 10.5220/0007558801930200
in Bibtex Style
@conference{iwemse18,
author={Q. Gao and Y. Su and L. B. Wu and Y. Zhou},
title={Forecasting Electricity Consumption of Appliances based on the Additive Factor Hidden Markov Model},
booktitle={Proceedings of the International Workshop on Environmental Management, Science and Engineering - Volume 1: IWEMSE,},
year={2018},
pages={193-200},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007558801930200},
isbn={978-989-758-344-5},
}
in EndNote Style
TY - CONF
JO - Proceedings of the International Workshop on Environmental Management, Science and Engineering - Volume 1: IWEMSE,
TI - Forecasting Electricity Consumption of Appliances based on the Additive Factor Hidden Markov Model
SN - 978-989-758-344-5
AU - Gao Q.
AU - Su Y.
AU - Wu L.
AU - Zhou Y.
PY - 2018
SP - 193
EP - 200
DO - 10.5220/0007558801930200