Benchmarking of Load Forecasting Methods Using Residential Smart Meter Data
As the access to consumption data available in household smart meters is now very common in several developed countries, this kind of information is assuming a providential role for different players in the energy sector. The proposed study was applied to data available from the Smart Meter Energy C...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/19/9844 |
| _version_ | 1797480635207516160 |
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| author | João C. Sousa Hermano Bernardo |
| author_facet | João C. Sousa Hermano Bernardo |
| author_sort | João C. Sousa |
| collection | DOAJ |
| description | As the access to consumption data available in household smart meters is now very common in several developed countries, this kind of information is assuming a providential role for different players in the energy sector. The proposed study was applied to data available from the Smart Meter Energy Consumption Data in the London Households dataset, provided by UK Power Networks, containing half-hourly readings from an original sample of 5567 households (71 households were hereby carefully selected after a justified filtering process). The main aim is to forecast the day—ahead load profile, based only on previous load values and some auxiliary variables. During this research different forecasting models are applied, tested and compared to allow comprehensive analyses integrating forecasting accuracy, processing times and the interpretation of the most influential features in each case. The selected models are based on Multivariate Adaptive Regression Splines, Random Forests and Artificial Neural Networks, and the accuracies resulted from each model are compared and confronted with a baseline (<i>Naïve</i> model). The different forecasting approaches being evaluated have been revealed to be effective, ensuring a mean reduction of 15% in Mean Absolute Error when compared to the baseline. Artificial Neural Networks proved to be the most accurate model for a major part of the residential consumers. |
| first_indexed | 2024-03-09T22:02:52Z |
| format | Article |
| id | doaj.art-bfce45a00e3949cfb43c57565ff44245 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:02:52Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-bfce45a00e3949cfb43c57565ff442452023-11-23T19:46:41ZengMDPI AGApplied Sciences2076-34172022-09-011219984410.3390/app12199844Benchmarking of Load Forecasting Methods Using Residential Smart Meter DataJoão C. Sousa0Hermano Bernardo1School of Technology and Management, Polytechnic of Leiria, 2411-901 Leiria, PortugalSchool of Technology and Management, Polytechnic of Leiria, 2411-901 Leiria, PortugalAs the access to consumption data available in household smart meters is now very common in several developed countries, this kind of information is assuming a providential role for different players in the energy sector. The proposed study was applied to data available from the Smart Meter Energy Consumption Data in the London Households dataset, provided by UK Power Networks, containing half-hourly readings from an original sample of 5567 households (71 households were hereby carefully selected after a justified filtering process). The main aim is to forecast the day—ahead load profile, based only on previous load values and some auxiliary variables. During this research different forecasting models are applied, tested and compared to allow comprehensive analyses integrating forecasting accuracy, processing times and the interpretation of the most influential features in each case. The selected models are based on Multivariate Adaptive Regression Splines, Random Forests and Artificial Neural Networks, and the accuracies resulted from each model are compared and confronted with a baseline (<i>Naïve</i> model). The different forecasting approaches being evaluated have been revealed to be effective, ensuring a mean reduction of 15% in Mean Absolute Error when compared to the baseline. Artificial Neural Networks proved to be the most accurate model for a major part of the residential consumers.https://www.mdpi.com/2076-3417/12/19/9844load forecastingsmart meterresidential consumptionRandom ForestArtificial Neural Networks |
| spellingShingle | João C. Sousa Hermano Bernardo Benchmarking of Load Forecasting Methods Using Residential Smart Meter Data Applied Sciences load forecasting smart meter residential consumption Random Forest Artificial Neural Networks |
| title | Benchmarking of Load Forecasting Methods Using Residential Smart Meter Data |
| title_full | Benchmarking of Load Forecasting Methods Using Residential Smart Meter Data |
| title_fullStr | Benchmarking of Load Forecasting Methods Using Residential Smart Meter Data |
| title_full_unstemmed | Benchmarking of Load Forecasting Methods Using Residential Smart Meter Data |
| title_short | Benchmarking of Load Forecasting Methods Using Residential Smart Meter Data |
| title_sort | benchmarking of load forecasting methods using residential smart meter data |
| topic | load forecasting smart meter residential consumption Random Forest Artificial Neural Networks |
| url | https://www.mdpi.com/2076-3417/12/19/9844 |
| work_keys_str_mv | AT joaocsousa benchmarkingofloadforecastingmethodsusingresidentialsmartmeterdata AT hermanobernardo benchmarkingofloadforecastingmethodsusingresidentialsmartmeterdata |