A Study of Machine Learning Techniques for Daily Solar Energy Forecasting Using Numerical Weather Models

Abstract

Abstract. Forecasting solar energy is becoming an important issue in the context of renewable energy sources and Machine Learning Algorithms play an important rule in this field. The prediction of solar energy can be addressed as a time series prediction problem using historical data. Also, solar energy forecasting can be derived from numerical weather prediction models (NWP). Our interest is focused on the latter approach. We focus on the problem of predicting solar energy from NWP computed from GEFS, the Global Ensemble Forecast System, which predicts meteorological variables for points in a grid. In this context, it can be useful to know how prediction accuracy improves depending on the number of grid nodes used as input for the machine learning techniques. However, using the variables from a large number of grid nodes can result in many attributes which might degrade the generalization performance of the learning algorithms. In this paper both issues are studied using data supplied by Kaggle for the State of Oklahoma comparing Support Vector Machines and Gradient Boosted Regression. Also, three different feature selection methods have been tested: Linear Correlation, the ReliefF algorithm and, a new method based on local information analysis.

Comparison Analysis of Machine Learning Techniques for Photovoltaic Prediction Using Weather Sensor Data

“…A classification can be made based on how the weather data are utilized as input. Three categories were observed, studies that only use weather forecast [5][6][7][8][9][10][11][12][13][14][15][16][17][18], those that use only weather observation [19][20][21][22][23][24][25], and those that use both forms of weather data [1,[26][27][28][29][30]. In the first category, planning and projection is required before the actual generation of solar energy, but they are highly correlated with the errors that meteorological stations can make in the forecasting.…”

Intelligent Distributed Computing

“…Ricardo Aler, Ricardo Martin, Jose Valls and Ines Galván in [1] develop machine learning algorithms for forecasting of solar energy in the contexts of renewable energy sources. The prediction of solar energy is derived from numerical weather prediction models, which predicts meteorological variables for nodes in a grid.…”

Uncertainties in global radiation time series forecasting using machine learning: The multilayer perceptron case

“…All these approaches are related to the machine learning application [22]. The most often used is the last presented method: the artificial neural network and particularly the multilayer perceptron (MLP [23]).…”