Hourly solar irradiance forecasting based on machine learning models

Fateh Nassim Melzi; Taieb Touati; Allou Same; Latifa Oukhellou

Hourly solar irradiance forecasting based on machine learning models

Fateh Nassim Melzi ¹ Taieb Touati ² Allou Same ² Latifa Oukhellou ²

2 IFSTTAR/COSYS/GRETTIA - Génie des Réseaux de Transport Terrestres et Informatique Avancée

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.

Keywords : IRRADIANCE FORECASTING MACHINE LEARNING STATISTICAL APPROACH

Document type :

Conference papers

Domain :

Statistics [stat] / Machine Learning [stat.ML]

Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01383793
Contributor : Ifsttar Cadic <>
Submitted on : Wednesday, October 19, 2016 - 11:25:46 AM
Last modification on : Wednesday, April 11, 2018 - 12:10:02 PM

Hourly solar irradiance forecasting based on machine learning models

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Hourly solar irradiance forecasting based on machine learning models

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