Time-series clustering and forecasting household electricity demand using smart meter data
This study forecasts electricity consumption in a smart grid environment. We present a bottom-up prediction method using a combination of forecasting values based on time-series clustering using advanced metering infrastructure (AMI) data, one of the core smart grid technologies. Remote data meterin...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484723002731 |
| _version_ | 1797781852922052608 |
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| author | Hyojeoung Kim Sujin Park Sahm Kim |
| author_facet | Hyojeoung Kim Sujin Park Sahm Kim |
| author_sort | Hyojeoung Kim |
| collection | DOAJ |
| description | This study forecasts electricity consumption in a smart grid environment. We present a bottom-up prediction method using a combination of forecasting values based on time-series clustering using advanced metering infrastructure (AMI) data, one of the core smart grid technologies. Remote data metering every 15 min to 1 h is possible with real-time communication on power generation information, consumption, and AMI development. Hence, its prediction is more challenging due to the large variation of each household’s electricity. These issues were solved by time-series clustering methods using Euclidean distances and Dynamic Time Warping distance. The auto-regressive integrated moving average (ARIMA), ARIMA exogenous (ARIMAX), double seasonal Holt–Winters (DSHW), trigonometric, Box–Cox transform, autoregressive moving average errors, trend and seasonal components (TBATS), neural network nonlinear autoregressive (NNAR), and nonlinear autoregressive exogenous (NARX) models were used for demand forecasting based on clustering. The result showed that the time-series clustering method performed better than that using the total amount of electricity demand regarding the mean absolute percentage error and root mean squared error.Hence, various exogenous variables were considered to improve model accuracy. The model considering exogenous variables—cooling degree day, humidity, insolation, indicator variables, and generation power consumption performed better than that without exogenous variables. |
| first_indexed | 2024-03-13T00:02:47Z |
| format | Article |
| id | doaj.art-5a22757c87324df78422d90be7408f32 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-03-13T00:02:47Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-5a22757c87324df78422d90be7408f322023-07-13T05:29:44ZengElsevierEnergy Reports2352-48472023-12-01941114121Time-series clustering and forecasting household electricity demand using smart meter dataHyojeoung Kim0Sujin Park1Sahm Kim2Department of Applied Statistics, Chung-Ang University, Seoul, Republic of KoreaDepartment of Applied Statistics, Chung-Ang University, Seoul, Republic of KoreaDepartment of Applied Statistics, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Republic of Korea; Corresponding author.This study forecasts electricity consumption in a smart grid environment. We present a bottom-up prediction method using a combination of forecasting values based on time-series clustering using advanced metering infrastructure (AMI) data, one of the core smart grid technologies. Remote data metering every 15 min to 1 h is possible with real-time communication on power generation information, consumption, and AMI development. Hence, its prediction is more challenging due to the large variation of each household’s electricity. These issues were solved by time-series clustering methods using Euclidean distances and Dynamic Time Warping distance. The auto-regressive integrated moving average (ARIMA), ARIMA exogenous (ARIMAX), double seasonal Holt–Winters (DSHW), trigonometric, Box–Cox transform, autoregressive moving average errors, trend and seasonal components (TBATS), neural network nonlinear autoregressive (NNAR), and nonlinear autoregressive exogenous (NARX) models were used for demand forecasting based on clustering. The result showed that the time-series clustering method performed better than that using the total amount of electricity demand regarding the mean absolute percentage error and root mean squared error.Hence, various exogenous variables were considered to improve model accuracy. The model considering exogenous variables—cooling degree day, humidity, insolation, indicator variables, and generation power consumption performed better than that without exogenous variables.http://www.sciencedirect.com/science/article/pii/S2352484723002731Time-series clusteringResidential electricity demandTime-series forecastingSmart meter dataWeather variables |
| spellingShingle | Hyojeoung Kim Sujin Park Sahm Kim Time-series clustering and forecasting household electricity demand using smart meter data Energy Reports Time-series clustering Residential electricity demand Time-series forecasting Smart meter data Weather variables |
| title | Time-series clustering and forecasting household electricity demand using smart meter data |
| title_full | Time-series clustering and forecasting household electricity demand using smart meter data |
| title_fullStr | Time-series clustering and forecasting household electricity demand using smart meter data |
| title_full_unstemmed | Time-series clustering and forecasting household electricity demand using smart meter data |
| title_short | Time-series clustering and forecasting household electricity demand using smart meter data |
| title_sort | time series clustering and forecasting household electricity demand using smart meter data |
| topic | Time-series clustering Residential electricity demand Time-series forecasting Smart meter data Weather variables |
| url | http://www.sciencedirect.com/science/article/pii/S2352484723002731 |
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