Evaluation of Machine Learning Methods on Large-Scale Spatiotemporal Data for Photovoltaic Power Prediction
The exponential increase in photovoltaic (PV) arrays installed globally, particularly given the intermittent nature of PV generation, has emphasized the need to accurately forecast the predicted output power of the arrays. Regardless of the length of the forecasts, the modeling of PV arrays is made...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-06-01
|
Series: | Energies |
Subjects: | |
Online Access: | https://www.mdpi.com/1996-1073/16/13/4908 |
_version_ | 1797591844412981248 |
---|---|
author | Evan Sauter Maqsood Mughal Ziming Zhang |
author_facet | Evan Sauter Maqsood Mughal Ziming Zhang |
author_sort | Evan Sauter |
collection | DOAJ |
description | The exponential increase in photovoltaic (PV) arrays installed globally, particularly given the intermittent nature of PV generation, has emphasized the need to accurately forecast the predicted output power of the arrays. Regardless of the length of the forecasts, the modeling of PV arrays is made difficult by their dependence on weather. Typically, the model projections are generated from datasets at one location across a couple of years. The purpose of this study was to compare the effectiveness of regression models in very short-term deterministic forecasts for spatiotemporal projections. The compiled dataset is unique given that it consists of weather and output power data of PVs located at five cities spanning 3 and 6 years in length. Gated recurrent unit (GRU) generalized the best for same-city and cross-city predictions, while long short-term memory (LSTM) and ensemble bagging had the best cross-city and same-city predictions, respectively. |
first_indexed | 2024-03-11T01:43:10Z |
format | Article |
id | doaj.art-94e240a1187f43aaa6e0b8786fa13283 |
institution | Directory Open Access Journal |
issn | 1996-1073 |
language | English |
last_indexed | 2024-03-11T01:43:10Z |
publishDate | 2023-06-01 |
publisher | MDPI AG |
record_format | Article |
series | Energies |
spelling | doaj.art-94e240a1187f43aaa6e0b8786fa132832023-11-18T16:27:37ZengMDPI AGEnergies1996-10732023-06-011613490810.3390/en16134908Evaluation of Machine Learning Methods on Large-Scale Spatiotemporal Data for Photovoltaic Power PredictionEvan Sauter0Maqsood Mughal1Ziming Zhang2Department of Electrical and Computer Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, USADepartment of Electrical and Computer Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, USADepartment of Electrical and Computer Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, MA 01609, USAThe exponential increase in photovoltaic (PV) arrays installed globally, particularly given the intermittent nature of PV generation, has emphasized the need to accurately forecast the predicted output power of the arrays. Regardless of the length of the forecasts, the modeling of PV arrays is made difficult by their dependence on weather. Typically, the model projections are generated from datasets at one location across a couple of years. The purpose of this study was to compare the effectiveness of regression models in very short-term deterministic forecasts for spatiotemporal projections. The compiled dataset is unique given that it consists of weather and output power data of PVs located at five cities spanning 3 and 6 years in length. Gated recurrent unit (GRU) generalized the best for same-city and cross-city predictions, while long short-term memory (LSTM) and ensemble bagging had the best cross-city and same-city predictions, respectively.https://www.mdpi.com/1996-1073/16/13/4908machine learningdeep learningphotovoltaic generation forecastingspatiotemporal regression |
spellingShingle | Evan Sauter Maqsood Mughal Ziming Zhang Evaluation of Machine Learning Methods on Large-Scale Spatiotemporal Data for Photovoltaic Power Prediction Energies machine learning deep learning photovoltaic generation forecasting spatiotemporal regression |
title | Evaluation of Machine Learning Methods on Large-Scale Spatiotemporal Data for Photovoltaic Power Prediction |
title_full | Evaluation of Machine Learning Methods on Large-Scale Spatiotemporal Data for Photovoltaic Power Prediction |
title_fullStr | Evaluation of Machine Learning Methods on Large-Scale Spatiotemporal Data for Photovoltaic Power Prediction |
title_full_unstemmed | Evaluation of Machine Learning Methods on Large-Scale Spatiotemporal Data for Photovoltaic Power Prediction |
title_short | Evaluation of Machine Learning Methods on Large-Scale Spatiotemporal Data for Photovoltaic Power Prediction |
title_sort | evaluation of machine learning methods on large scale spatiotemporal data for photovoltaic power prediction |
topic | machine learning deep learning photovoltaic generation forecasting spatiotemporal regression |
url | https://www.mdpi.com/1996-1073/16/13/4908 |
work_keys_str_mv | AT evansauter evaluationofmachinelearningmethodsonlargescalespatiotemporaldataforphotovoltaicpowerprediction AT maqsoodmughal evaluationofmachinelearningmethodsonlargescalespatiotemporaldataforphotovoltaicpowerprediction AT zimingzhang evaluationofmachinelearningmethodsonlargescalespatiotemporaldataforphotovoltaicpowerprediction |