Hourly Solar Irradiance Forecasting Based on Machine Learning Models
Hourly Solar Irradiance Forecasting Based on Machine Learning Models
复制标题
基于机器学习模型的每小时太阳辐照度预测
DOI：
10.1109/icmla.2016.0078
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
L. Oukhellou
中科院分区：
文献类型：
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作者：
F. Melzi;Taieb Touati;A. Samé;L. Oukhellou
近年来，许多研究都进行了智能电表数据的使用开发决策工具，包括分析，预测和显示的目的。预测能源生产或预测能源消费需求确实是城市利益攸关方（电力公司和城市规划者）的核心问题。这些问题有助于确保能源资源的有效规划和优化。本文研究了在机器学习（ML）框架内，使用相似性方法（SIM），支持向量机（SVM）和神经网络（NN）预测逐时太阳辐照度的问题。这些方法依赖于一种方法，该方法考虑了预测日的前几个小时以及历史上具有相同日照时数的天数。这项研究是在巴黎郊区Alfortville收集的真实的数据集上进行的。与两种时间序列方法，即朴素方法和自回归移动平均模型（阿尔马）进行了比较。本研究是发展逐时太阳辐照度预报混合模式的第一步。
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.