Fast short-term global solar irradiance forecasting with wrapper mutual information

Bouzgou, Hassen; Gueymard, Christian A.

doi:10.1016/j.renene.2018.10.096

Cited by 58 publications

(16 citation statements)

References 37 publications

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“…The results show that multi-scale decomposition significantly improves forecasting results, both using ANNs and the hybrid model. Bouzgou et al [13] design a Wrapper Mutual Information Methodology (WMIM) optimization approach by exploiting Extreme Learning Machine (ELM) regression technique to i) investigate the effect of the mutual information measure between the historical variables and the targeted future GHI value, and ii) select the best possible combination of historical variables from the existing time series. Experimental results highlight that the ELM model, combined with WMIM, provides the same performances of the more conventional Multi Layer Perceptron (MLP) but lower computing time.…”

Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Neural Networks Techniques to Forecast Global Horizontal Irradiance

et al. 2021

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Due to the continuous increasing importance of renewable energy sources as an alternative to fossil fuels, to contrast air pollution and global warming, the prediction of Global Horizontal Irradiation (GHI), one of the main parameters determining solar energy production of photovoltaic systems, represents an attractive topic nowadays. Solar irradiance is determined by deterministic factors (i.e. the position of the sun) and stochastic factors (i.e. the presence of clouds). Since the stochastic element is difficult to model, this problem can benefit from machine learning techniques, like artificial neural networks. This work proposes a methodology to forecast GHI in short-(i.e. from 15 min to 60 min) and mid-term (i.e. from 60 to 120 min) time horizons. For this purpose, we designed, optimised and compared four neural network architectures for time-series forecasting, respectively based on: i) Non-Linear Autoregressive, ii) Feed-Forward, iii) Long Short-Term Memory and iv) Echo State Network. The original data-set, consisting of GHI values sampled every 15min, has been pre-processed by applying different filtering techniques. Our results analysis compares the performance of the proposed neural networks identifying the best in terms of error rate and forecast horizon. This analysis highlights that the clear-sky index results the preferred filtering technique by giving greatly improvements in data-set pre-processing, and Echo State Network gives best accuracy results.

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Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Neural Networks Techniques to Forecast Global Horizontal Irradiance

et al. 2021

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“…Based on the monthly precipitation data of the meteorological stations, we calculated the correlation coefficient (R), the determination coefficient (R 2 ), the root mean square error (RMSE), the mean absolute error (MAE), and the normalized mean square error (NMSE) to evaluate the simulation results [49,50,56,57]. The methods are as follows:…”

Section: The Evaluation Of Downscaling Simulationmentioning

confidence: 99%

Downscaling Precipitation in the Data-Scarce Inland River Basin of Northwest China Based on Earth System Data Products

Zuo

Chen

et al. 2019

Atmosphere

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Precipitation is a key climatic variable that connects the processes of atmosphere and land surface, and it plays a leading role in the water cycle. However, the vast area of Northwest China, its complex geographical environment, and its scarce observation data make it difficult to deeply understand the temporal and spatial variation of precipitation. This paper establishes a statistical downscaling model to downscale the monthly precipitation in the inland river basin of Northwest China with the Tarim River Basin (TRB) as a typical representation. This method combines polynomial regression and machine learning, and it uses the batch gradient descent (BGD) algorithm to train the regression model. We downscale the monthly precipitation and obtain a dataset from January 2001 to December 2017 with a spatial resolution of 1 km × 1 km. The results show that the downscaling model presents a good performance in precipitation simulation with a high resolution, and it is more effective than ordinary polynomial regression. We also investigate the temporal and spatial variations of precipitation in the TRB based on the downscaling dataset. Analyses illustrate that the annual precipitation in the southern foothills of the Tianshan Mountains and the North Kunlun Mountains showed a significant upward trend during the study periods, while the annual precipitation in the central plains presented a significant downward trend.

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“…At present, the feature selection is mainly divided into two methods, which are wrapper and filter. As for the wrapper method, Bouzgou et al [6] proposed an approach combining mutual information and a limit learning machine, and the experiment proves that the proposed method of packaging mutual information is superior to the method of multi-layer perceptron in the calculation efficiency and error difference percentage. As for the filter method, binary transformation of the optimization algorithm has more advantages in solving problems.…”

Section: Introductionmentioning

confidence: 99%

A Combination Strategy of Feature Selection Based on an Integrated Optimization Algorithm and Weighted K-Nearest Neighbor to Improve the Performance of Network Intrusion Detection

Przystupa

Fang

et al. 2020

Electronics

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With the widespread use of the Internet, network security issues have attracted more and more attention, and network intrusion detection has become one of the main security technologies. As for network intrusion detection, the original data source always has a high dimension and a large amount of data, which greatly influence the efficiency and the accuracy. Thus, both feature selection and the classifier then play a significant role in raising the performance of network intrusion detection. This paper takes the results of classification optimization of weighted K-nearest neighbor (KNN) with those of the feature selection algorithm into consideration, and proposes a combination strategy of feature selection based on an integrated optimization algorithm and weighted KNN, in order to improve the performance of network intrusion detection. Experimental results show that the weighted KNN can increase the efficiency at the expense of a small amount of the accuracy. Thus, the proposed combination strategy of feature selection based on an integrated optimization algorithm and weighted KNN can then improve both the efficiency and the accuracy of network intrusion detection.

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Fast short-term global solar irradiance forecasting with wrapper mutual information

Cited by 58 publications

References 37 publications

Comparative Analysis of Neural Networks Techniques to Forecast Global Horizontal Irradiance

Comparative Analysis of Neural Networks Techniques to Forecast Global Horizontal Irradiance

Downscaling Precipitation in the Data-Scarce Inland River Basin of Northwest China Based on Earth System Data Products

A Combination Strategy of Feature Selection Based on an Integrated Optimization Algorithm and Weighted K-Nearest Neighbor to Improve the Performance of Network Intrusion Detection

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