Forecasting the Monash Microgrid for the IEEE-CIS Technical Challenge
Effective operation of a microgrid depends critically on accurate forecasting of its components. Recently, internet forecasting competitions have been used to determine the best methods for energy forecasting, with some competitions having a special focus on microgrids and COVID-19 energy-use foreca...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/3/1050 |
| _version_ | 1797624786233327616 |
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| author | Richard Bean |
| author_facet | Richard Bean |
| author_sort | Richard Bean |
| collection | DOAJ |
| description | Effective operation of a microgrid depends critically on accurate forecasting of its components. Recently, internet forecasting competitions have been used to determine the best methods for energy forecasting, with some competitions having a special focus on microgrids and COVID-19 energy-use forecasting. This paper describes forecasting for the IEEE Computational Intelligence Society 3rd Technical Challenge, which required predicting solar and building loads of a microgrid system at Monash University for the month of November 2020. The forecast achieved the lowest error rate in the competition. We review the literature on recent energy forecasting competitions and metrics and explain how the solution drew from top-ranked solutions in previous energy forecasting competitions such as the Global Energy Forecasting Competition series. The techniques can be reapplied in other forecasting endeavours, while approaches to some of the time-series forecasting are more ad hoc and specific to the competition. Novel thresholding approaches were used to improve the quality of the input data. As the training and evaluation phase of the challenge occurred during COVID-19 lockdown and reopening, the building demand was subject to pandemic-related effects. Finally, we assess other data sources which would have improved the model forecast skill such as data from different numerical weather prediction (NWP) models, solar observations, and high-resolution price and demand data in the vicinity of the campus. |
| first_indexed | 2024-03-11T09:47:23Z |
| format | Article |
| id | doaj.art-ee82bbb53155455381a17aab983a45d9 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T09:47:23Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-ee82bbb53155455381a17aab983a45d92023-11-16T16:31:56ZengMDPI AGEnergies1996-10732023-01-01163105010.3390/en16031050Forecasting the Monash Microgrid for the IEEE-CIS Technical ChallengeRichard Bean0School of Information Technology and Electrical Engineering, University of Queensland, St Lucia 4072, AustraliaEffective operation of a microgrid depends critically on accurate forecasting of its components. Recently, internet forecasting competitions have been used to determine the best methods for energy forecasting, with some competitions having a special focus on microgrids and COVID-19 energy-use forecasting. This paper describes forecasting for the IEEE Computational Intelligence Society 3rd Technical Challenge, which required predicting solar and building loads of a microgrid system at Monash University for the month of November 2020. The forecast achieved the lowest error rate in the competition. We review the literature on recent energy forecasting competitions and metrics and explain how the solution drew from top-ranked solutions in previous energy forecasting competitions such as the Global Energy Forecasting Competition series. The techniques can be reapplied in other forecasting endeavours, while approaches to some of the time-series forecasting are more ad hoc and specific to the competition. Novel thresholding approaches were used to improve the quality of the input data. As the training and evaluation phase of the challenge occurred during COVID-19 lockdown and reopening, the building demand was subject to pandemic-related effects. Finally, we assess other data sources which would have improved the model forecast skill such as data from different numerical weather prediction (NWP) models, solar observations, and high-resolution price and demand data in the vicinity of the campus.https://www.mdpi.com/1996-1073/16/3/1050time-series forecastingsolar forecastingrenewable energyrandom forests |
| spellingShingle | Richard Bean Forecasting the Monash Microgrid for the IEEE-CIS Technical Challenge Energies time-series forecasting solar forecasting renewable energy random forests |
| title | Forecasting the Monash Microgrid for the IEEE-CIS Technical Challenge |
| title_full | Forecasting the Monash Microgrid for the IEEE-CIS Technical Challenge |
| title_fullStr | Forecasting the Monash Microgrid for the IEEE-CIS Technical Challenge |
| title_full_unstemmed | Forecasting the Monash Microgrid for the IEEE-CIS Technical Challenge |
| title_short | Forecasting the Monash Microgrid for the IEEE-CIS Technical Challenge |
| title_sort | forecasting the monash microgrid for the ieee cis technical challenge |
| topic | time-series forecasting solar forecasting renewable energy random forests |
| url | https://www.mdpi.com/1996-1073/16/3/1050 |
| work_keys_str_mv | AT richardbean forecastingthemonashmicrogridfortheieeecistechnicalchallenge |