Deep hybrid neural net (DHN-Net) for minute-level day-ahead solar and wind power forecast in a decarbonized power system
The need to reduce global carbon emissions has led to a significant increase in clean energy globally. While renewable energy penetration into energy grids and power systems is increasing in many countries, the intermittency and stochastic nature of wind and solar energy resources is still a major c...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-10-01
|
| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484723009721 |
| _version_ | 1797388786169020416 |
|---|---|
| author | Olusola Bamisile Dongsheng Cai Humphrey Adun Chukwuebuka Ejiyi Olufunso Alowolodu Benjamin Ezurike Qi Huang |
| author_facet | Olusola Bamisile Dongsheng Cai Humphrey Adun Chukwuebuka Ejiyi Olufunso Alowolodu Benjamin Ezurike Qi Huang |
| author_sort | Olusola Bamisile |
| collection | DOAJ |
| description | The need to reduce global carbon emissions has led to a significant increase in clean energy globally. While renewable energy penetration into energy grids and power systems is increasing in many countries, the intermittency and stochastic nature of wind and solar energy resources is still a major challenge. These can affect the safety, stability, and reliability of the energy grid. In existing works of literature, the forecast and prediction of wind energy, solar power, wind power, and solar energy with various models have been considered independently. However, with the rise in solar power and wind power penetration, there exists a gap in literature on the development of models that can simultaneously forecast solar and wind power production. In this paper, two deep hybrid neural networks (DHN-Net) models are developed for the simultaneous forecast of wind and solar power. The novelty of this study is further strengthened as a minute-level timestep is considered for the application of the models developed. The models are trained and tested with data collected from Zone 1 of four different power system operators in the USA. The two DHN-Net models are built on the foundation of artificial, convolutional, and recurrent neural networks (ANN, CNN, and RNN). Results from this study show that the two DHN-Nets can accurately forecast solar and wind power with an R-squared (r2) value of 0.9915, RMSE of 0.01920, and MAE of 0.00736 for data collected from PJM_Zone1. The DHN-Net models recorded a better performance when compared to the benchmark results in literature. |
| first_indexed | 2024-03-08T22:45:52Z |
| format | Article |
| id | doaj.art-006ef2c841b04873a1375b20cb594b07 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-03-08T22:45:52Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-006ef2c841b04873a1375b20cb594b072023-12-17T06:39:28ZengElsevierEnergy Reports2352-48472023-10-01911631172Deep hybrid neural net (DHN-Net) for minute-level day-ahead solar and wind power forecast in a decarbonized power systemOlusola Bamisile0Dongsheng Cai1Humphrey Adun2Chukwuebuka Ejiyi3Olufunso Alowolodu4Benjamin Ezurike5Qi Huang6Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Research Center, Chengdu University of Technology, Sichuan P.R., 610059, ChinaSichuan Industrial Internet Intelligent Monitoring and Application Engineering Research Center, Chengdu University of Technology, Sichuan P.R., 610059, China; Corresponding author.Energy System Engineering Department, Cyprus International University, Haspolat-Lefkosa, Mersin 10, KKTC, TurkeySchool of Information and Software Engineering, University of Electronic Science and Technology of China, Sichuan P.R., 611731, ChinaDepartment of Cybersecurity, School of Computing, Federal University of Technology Akure, Ondo State P.M.B. 704, NigeriaDepartment of Mechanical/Mechatronic Engineering, Alex Ekwueme Federal University, Ndufu-Alike, Abakaliki P.M.B. 1010, NigeriaSichuan Industrial Internet Intelligent Monitoring and Application Engineering Research Center, Chengdu University of Technology, Sichuan P.R., 610059, ChinaThe need to reduce global carbon emissions has led to a significant increase in clean energy globally. While renewable energy penetration into energy grids and power systems is increasing in many countries, the intermittency and stochastic nature of wind and solar energy resources is still a major challenge. These can affect the safety, stability, and reliability of the energy grid. In existing works of literature, the forecast and prediction of wind energy, solar power, wind power, and solar energy with various models have been considered independently. However, with the rise in solar power and wind power penetration, there exists a gap in literature on the development of models that can simultaneously forecast solar and wind power production. In this paper, two deep hybrid neural networks (DHN-Net) models are developed for the simultaneous forecast of wind and solar power. The novelty of this study is further strengthened as a minute-level timestep is considered for the application of the models developed. The models are trained and tested with data collected from Zone 1 of four different power system operators in the USA. The two DHN-Net models are built on the foundation of artificial, convolutional, and recurrent neural networks (ANN, CNN, and RNN). Results from this study show that the two DHN-Nets can accurately forecast solar and wind power with an R-squared (r2) value of 0.9915, RMSE of 0.01920, and MAE of 0.00736 for data collected from PJM_Zone1. The DHN-Net models recorded a better performance when compared to the benchmark results in literature.http://www.sciencedirect.com/science/article/pii/S2352484723009721Decarbonizaed energy gridsDeep hybird neural networkForecastSolar powerWind power |
| spellingShingle | Olusola Bamisile Dongsheng Cai Humphrey Adun Chukwuebuka Ejiyi Olufunso Alowolodu Benjamin Ezurike Qi Huang Deep hybrid neural net (DHN-Net) for minute-level day-ahead solar and wind power forecast in a decarbonized power system Energy Reports Decarbonizaed energy grids Deep hybird neural network Forecast Solar power Wind power |
| title | Deep hybrid neural net (DHN-Net) for minute-level day-ahead solar and wind power forecast in a decarbonized power system |
| title_full | Deep hybrid neural net (DHN-Net) for minute-level day-ahead solar and wind power forecast in a decarbonized power system |
| title_fullStr | Deep hybrid neural net (DHN-Net) for minute-level day-ahead solar and wind power forecast in a decarbonized power system |
| title_full_unstemmed | Deep hybrid neural net (DHN-Net) for minute-level day-ahead solar and wind power forecast in a decarbonized power system |
| title_short | Deep hybrid neural net (DHN-Net) for minute-level day-ahead solar and wind power forecast in a decarbonized power system |
| title_sort | deep hybrid neural net dhn net for minute level day ahead solar and wind power forecast in a decarbonized power system |
| topic | Decarbonizaed energy grids Deep hybird neural network Forecast Solar power Wind power |
| url | http://www.sciencedirect.com/science/article/pii/S2352484723009721 |
| work_keys_str_mv | AT olusolabamisile deephybridneuralnetdhnnetforminuteleveldayaheadsolarandwindpowerforecastinadecarbonizedpowersystem AT dongshengcai deephybridneuralnetdhnnetforminuteleveldayaheadsolarandwindpowerforecastinadecarbonizedpowersystem AT humphreyadun deephybridneuralnetdhnnetforminuteleveldayaheadsolarandwindpowerforecastinadecarbonizedpowersystem AT chukwuebukaejiyi deephybridneuralnetdhnnetforminuteleveldayaheadsolarandwindpowerforecastinadecarbonizedpowersystem AT olufunsoalowolodu deephybridneuralnetdhnnetforminuteleveldayaheadsolarandwindpowerforecastinadecarbonizedpowersystem AT benjaminezurike deephybridneuralnetdhnnetforminuteleveldayaheadsolarandwindpowerforecastinadecarbonizedpowersystem AT qihuang deephybridneuralnetdhnnetforminuteleveldayaheadsolarandwindpowerforecastinadecarbonizedpowersystem |