Short-term forecasting of individual household electricity loads with investigating impact of data resolution and forecast horizon

Smart grid components such as smart home and battery energy management systems, high penetration of renewable energy systems, and demand response activities, require accurate electricity demand forecasts for the successful operation of the electricity distribution networks. For example, in order to...

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Main Authors: Yildiz Baran, Bilbao Jose I., Dore Jonathon, Sproul Alistair B.
Format: Article
Language:English
Published: EDP Sciences 2018-01-01
Series:Renewable Energy and Environmental Sustainability
Online Access:https://www.rees-journal.org/articles/rees/full_html/2018/01/rees180003s/rees180003s.html
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spelling doaj-a160c2c811a94d8ab36ffd3a99e509dc2020-11-25T04:00:15ZengEDP SciencesRenewable Energy and Environmental Sustainability2493-94392018-01-013310.1051/rees/2018003rees180003sShort-term forecasting of individual household electricity loads with investigating impact of data resolution and forecast horizonYildiz Baranhttps://orcid.org/0000-0003-3555-855XBilbao Jose I.Dore JonathonSproul Alistair B.Smart grid components such as smart home and battery energy management systems, high penetration of renewable energy systems, and demand response activities, require accurate electricity demand forecasts for the successful operation of the electricity distribution networks. For example, in order to optimize residential PV generation and electricity consumption and plan battery charge-discharge regimes by scheduling household appliances, forecasts need to target and be tailored to individual household electricity loads. The recent uptake of smart meters allows easier access to electricity readings at very fine resolutions; hence, it is possible to utilize this source of available data to create forecast models. In this paper, models which predominantly use smart meter data alongside with weather variables, or smart meter based models (SMBM), are implemented to forecast individual household loads. Well-known machine learning models such as artificial neural networks (ANN), support vector machines (SVM) and Least-Square SVM are implemented within the SMBM framework and their performance is compared. The analysed household stock consists of 14 households from the state of New South Wales, Australia, with at least a year worth of 5 min. resolution data. In order for the results to be comparable between different households, our study first investigates household load profiles according to their volatility and reveals the relationship between load standard deviation and forecast performance. The analysis extends previous research by evaluating forecasts over four different data resolution; 5, 15, 30 and 60 min, each resolution analysed for four different horizons; 1, 6, 12 and 24 h ahead. Both, data resolution and forecast horizon, proved to have significant impact on the forecast performance and the obtained results provide important insights for the operation of various smart grid applications. Finally, it is shown that the load profile of some households vary significantly across different days; as a result, providing a single model for the entire period may result in limited performance. By the use of a pre-clustering step, similar daily load profiles are grouped together according to their standard deviation, and instead of applying one SMBM for the entire data-set of a particular household, separate SMBMs are applied to each one of the clusters. This preliminary clustering step increases the complexity of the analysis however it results in significant improvements in forecast performance.https://www.rees-journal.org/articles/rees/full_html/2018/01/rees180003s/rees180003s.html
collection DOAJ
language English
format Article
sources DOAJ
author Yildiz Baran
Bilbao Jose I.
Dore Jonathon
Sproul Alistair B.
spellingShingle Yildiz Baran
Bilbao Jose I.
Dore Jonathon
Sproul Alistair B.
Short-term forecasting of individual household electricity loads with investigating impact of data resolution and forecast horizon
Renewable Energy and Environmental Sustainability
author_facet Yildiz Baran
Bilbao Jose I.
Dore Jonathon
Sproul Alistair B.
author_sort Yildiz Baran
title Short-term forecasting of individual household electricity loads with investigating impact of data resolution and forecast horizon
title_short Short-term forecasting of individual household electricity loads with investigating impact of data resolution and forecast horizon
title_full Short-term forecasting of individual household electricity loads with investigating impact of data resolution and forecast horizon
title_fullStr Short-term forecasting of individual household electricity loads with investigating impact of data resolution and forecast horizon
title_full_unstemmed Short-term forecasting of individual household electricity loads with investigating impact of data resolution and forecast horizon
title_sort short-term forecasting of individual household electricity loads with investigating impact of data resolution and forecast horizon
publisher EDP Sciences
series Renewable Energy and Environmental Sustainability
issn 2493-9439
publishDate 2018-01-01
description Smart grid components such as smart home and battery energy management systems, high penetration of renewable energy systems, and demand response activities, require accurate electricity demand forecasts for the successful operation of the electricity distribution networks. For example, in order to optimize residential PV generation and electricity consumption and plan battery charge-discharge regimes by scheduling household appliances, forecasts need to target and be tailored to individual household electricity loads. The recent uptake of smart meters allows easier access to electricity readings at very fine resolutions; hence, it is possible to utilize this source of available data to create forecast models. In this paper, models which predominantly use smart meter data alongside with weather variables, or smart meter based models (SMBM), are implemented to forecast individual household loads. Well-known machine learning models such as artificial neural networks (ANN), support vector machines (SVM) and Least-Square SVM are implemented within the SMBM framework and their performance is compared. The analysed household stock consists of 14 households from the state of New South Wales, Australia, with at least a year worth of 5 min. resolution data. In order for the results to be comparable between different households, our study first investigates household load profiles according to their volatility and reveals the relationship between load standard deviation and forecast performance. The analysis extends previous research by evaluating forecasts over four different data resolution; 5, 15, 30 and 60 min, each resolution analysed for four different horizons; 1, 6, 12 and 24 h ahead. Both, data resolution and forecast horizon, proved to have significant impact on the forecast performance and the obtained results provide important insights for the operation of various smart grid applications. Finally, it is shown that the load profile of some households vary significantly across different days; as a result, providing a single model for the entire period may result in limited performance. By the use of a pre-clustering step, similar daily load profiles are grouped together according to their standard deviation, and instead of applying one SMBM for the entire data-set of a particular household, separate SMBMs are applied to each one of the clusters. This preliminary clustering step increases the complexity of the analysis however it results in significant improvements in forecast performance.
url https://www.rees-journal.org/articles/rees/full_html/2018/01/rees180003s/rees180003s.html
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