Hourly Solar Irradiance Forecasting Based on Machine Learning Models
2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)(2016)
摘要
In recent years, many research studies are conducted into the use of smart meters data for developping 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.
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关键词
machine learning models,smart meters data,decision making tools,forecasting energy generation,forecasting energy consumption demand,urban stakeholders,electricity companies,urban planners,energy resources,ML framework,similarity method,SIM,support vector machine,SVM,neural network,NN,Naive method,autoregressive moving average model,ARMA,hourly solar irradiance forecasting hybrid models
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