Photovoltaic power prediction using artificial neural networks and numerical weather data
| Autor: | Lara Febrero Garrido, José García, Ana Ogando Martínez, Francisco Manuel Troncoso Pastoriza, Javier López Gómez, Enrique Granada Álvarez |
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| Rok vydání: | 2020 |
| Předmět: |
Weather monitoring
010504 meteorology & atmospheric sciences Computer science 020209 energy Artificial Neural Network (ANN) Geography Planning and Development Real-time computing 2509.19 Análisis del tiempo meteorológico TJ807-830 02 engineering and technology Management Monitoring Policy and Law TD194-195 01 natural sciences Renewable energy sources 3306.02 Aplicaciones Eléctricas Data assimilation Photovoltaic power 0202 electrical engineering electronic engineering information engineering GE1-350 0105 earth and related environmental sciences Wind power Artificial neural network Environmental effects of industries and plants Renewable Energy Sustainability and the Environment business.industry Photovoltaic system Numerical weather prediction Power (physics) Renewable energy Environmental sciences Electricity generation Weather data 1203.04 Inteligencia Artificial 2509.09 Predicción Numérica Meteorológica Numerical Weather Prediction (NWP) business Global Data Assimilation System (GDAS) |
| Zdroj: | Investigo. Repositorio Institucional de la Universidade de Vigo Universidade de Vigo (UVigo) Sustainability, Vol 12, Iss 10295, p 10295 (2020) Sustainability Volume 12 Issue 24 RUC. Repositorio da Universidade da Coruña instname RUC: Repositorio da Universidade da Coruña Universidade da Coruña (UDC) |
| Popis: | [Abstract] The monitoring of power generation installations is key for modelling and predicting their future behaviour. Many renewable energy generation systems, such as photovoltaic panels and wind turbines, strongly depend on weather conditions. However, in situ measurements of relevant weather variables are not always taken into account when designing monitoring systems, and only power output is available. This paper aims to combine data from a Numerical Weather Prediction model with machine learning tools in order to accurately predict the power generation from a photovoltaic system. An Artificial Neural Network (ANN) model is used to predict power outputs from a real installation located in Puglia (southern Italy) using temperature and solar irradiation data taken from the Global Data Assimilation System (GDAS) sflux model outputs. Power outputs and weather monitoring data from the PV installation are used as a reference dataset. Three training and testing scenarios are designed. In the first one, weather data monitoring is used to both train the ANN model and predict power outputs. In the second one, training is done with monitoring data, but GDAS data is used to predict the results. In the last set, both training and result prediction are done by feeding GDAS weather data into the ANN model. The results show that the tested numerical weather model can be combined with machine learning tools to model the output of PV systems with less than 10% error, even when in situ weather measurements are not available. Universidade de Vigo; 00VI 131H 641.02 This investigation article was partially supported by the University of Vigo through the grant Convocatoria de Axudas á Investigación 2018: Axudas Predoutorais UVigo 2018 (grant number 00VI 131H 641.02). This investigation article was also partially supported by the Ministry of Universities of the Spanish Government through the grant Ayudas para la Formación de Profesorado Universitario: Convocatoria 2017 (grant number FPU17/01834). This investigation article was also partially supported by the Ministry of Universities of the Spanish Government by means of the SMARTHERM (Project: RTI2018-096296-B-C2) |
| Databáze: | OpenAIRE |
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