A Proposed Model to Forecast Hourly Global Solar Irradiation Based on Satellite Derived Data, Deep Learning and Machine Learning Approaches

Abstract

An accurate short-term global solar irradiation (GHI) forecast is essential for integrating the photovoltaic systems into the electricity grid by reducing some of the problems caused by the intermittency of solar energy, including rapid fluctuations in energy, management storage, and the high costs of electricity. In this paper, the authors proposed a new hybrid approach to forecast hourly GHI for the Al-Hoceima city, Morocco. For this purpose, a deep long short-term memory network is trained on a combination of the hourly GHI ground measurements from the meteorological station of Al-Hoceima and the satellite-derived GHI from the neighbouring pixels of the point of interest. Xgboost, Random Forest, and Recursive Feature Elimination with cross-validation were used to select the most relevant features, the lagged satellite-derived GHI around the point of interest, as input to the proposed model where the best forecasting model is selected using the Grid Search algorithm. The simulation and results showed that the proposed approach gives high performance and outperformed other benchmark approaches.

Review on Spatio-Temporal Solar Forecasting Methods Driven by In Situ Measurements or Their Combination with Satellite and Numerical Weather Prediction (NWP) Estimates

“…Benamrou et al [26] applied a Recursive Feature Elimination (RFE) algorithm for the selection of relevant pixels based on information from the surrounding pixels found for a station, with cross-validation being used with XGBoost [109] to choose the best selection variant. The output of this process is the input of the deep learning method LSTM for the prediction of GHI for the next 1 to 4 h.…”

“…Thus, naturally, the exploitation and proposal of different strategies can be found in various spatio-temporal solar forecasting works. In this line, many works implement input selection methods based on user-defined criteria, such as distance (e.g., [89,103,129]) or the degree of correlation (e.g., [41,105,106]) to the target site, the importance of features [26,106], usually derived from a model, or according to local wind patterns (e.g., [14,40,85]).…”

“…In this line, Yang et al [14] deployed a LASSO model to process up to 256 inputs (16 lags from 16 ground GHI sensors) and test the impact of varying the number of neighboring sites and lags considered; the use of wind information to select which neighbors to include in the model is also explored. There are also works that explore lower-dimension representations of the whole input set, seeking to retain the most relevant information, by using methods such as Self-Organizing Map (SOM) [19], Recursive Feature Elimination (RFE) [26], and Principal Component Analysis (PCA) [62,65]. In this regard, Licciardi et al [62] reduced the dimensionality of a set of 125 inputs (a 5-by-5 satellite-derived GHI grid, with 5 lags for each pixel) down to five inputs using a neural network-based non-linear PCA, which was shown to surpass three linear variations of PCA.…”

Analysis and Prediction of Tourism Data with Deep Learning

“…During the period, the reference materials include oral recommendation from friends, browsing in the circle of friends, personal preferences, OTA evaluation, work arrangements, etc. When tourists determine the tourist cities and scenic spots, it is related to the source of this topic, that is, to choose the time of travel and then arrange accommodation [5][6]. When determining the travel time, passengers will consider the weather conditions of the day.…”

Effect of Feature Selection on the Prediction of Direct Normal Irradiance

“…projects have provided various solar radiation and other meteorological data for various regions worldwide, such as the National Solar Radiation DataBase (NSRDB) [6][7][8] , the Prediction Of Worldwide Energy Resources (POWER) [9] , the Copernicus Atmosphere Monitoring Service (CAMS) [10,11] , and the global meteorological database METEONORM [12,13] . Machine learning and deep learning algorithms have gained significant attention in recent decades for their capacity to analyze arbitrary non-linear connections [14][15][16][17] .…”