Hourly solar irradiance forecasting based on machine learning models

Résumé : In recent years, many research studies are conducted into the use of smart meters data for developing decision-making tools including both analytical, forecasting and display purposes. Forecasting energy generation or forecasting energy consumption demand are indeed central problems for urban stakeholders (electricity companies and urban planners). These issues are helpful to allow them ensuring an efficient planning and optimization of energy resources. This paper investigates the problem for forecasting the hourly solar irradiance within a Machine Learning (ML) framework using Similarity method (SIM), Support Vector Machine (SVM) and Neural Network (NN). These approaches rely on a methodology which takes into account the previous hours of the predicting day and also the days having the same number of sunshine hours in the history. The study is conducted on a real data set collected on the Paris suburb of Alfortville. A comparison with two time series approaches namely Naive method and Autoregressive Moving Average Model (ARMA) is performed. This study is the first step towards the development of the hourly solar irradiance forecasting hybrid models.
Type de document :
Communication dans un congrès
ICMLA 2016 - IEEE 15th International Conference on Machine Learning and Applications, Dec 2016, Anaheim, United States. ICMLA 2016 - IEEE 15th International Conference on Machine Learning and Applications, 6p, 2016
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Soumis le : mercredi 19 octobre 2016 - 11:25:46
Dernière modification le : mercredi 11 avril 2018 - 12:10:02

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  • HAL Id : hal-01383793, version 1

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Fateh Nassim Melzi, Taieb Touati, Allou Same, Latifa Oukhellou. Hourly solar irradiance forecasting based on machine learning models. ICMLA 2016 - IEEE 15th International Conference on Machine Learning and Applications, Dec 2016, Anaheim, United States. ICMLA 2016 - IEEE 15th International Conference on Machine Learning and Applications, 6p, 2016. 〈hal-01383793〉

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