Assessment of different combinations of meteorological parameters for predicting daily global solar radiation using artificial neural networks

Mghouchi, Y. El; Chham, E.; Zemmouri, El Moukhtar; Bouardi, Abdelmajid El

doi:10.1016/j.buildenv.2018.12.055

Cited by 48 publications

(20 citation statements)

References 43 publications

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“…For this purpose, among the novelty of this study, the new relationships we have built to forecast the PMs concentrations and the AQI based on the best combinations of predictor variables. (7) For local communities, it is important to nd out which is the level of pollutants in the air, both from o cial and independent networks of sensors/stations, helping the decision makers to develop programs and implement proper measures and regulations to reduce air pollution. So, to more enhance the performance of the developed models, at least one of other meteorological parameters (solar radiation, wind speed and direction, cloudiness, etc.)…”

Section: Discussionmentioning

confidence: 99%

Air pollution forecasting and modelling using hybrid machine learning for Craiova City in Romania

Udriștioiu

MGHOUGHI

Yıldızhan

2022

Preprint

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Poor air quality affects human health and exacerbates climate change, inducing temperature variations. Therefore, it is important to understand at local level the interactions between climate variability and air pollution to mitigate the effects of air pollution on human health. The aim of this paper is to explore the correlations between meteorological parameters, temperature (T), humidity (H) and air pressure (P) and particulate matter concentrations (PM1, PM2.5, PM10). Also, it is investigated the correlation between PM 2.5 and PM10, between noise, CO2, and PM concentrations. For this purpose, five hybrid Machine Learning models were employed to forecast the PMs concentrations and then the Air Quality Index (AQI). The data set was provided by an independent network of sensors for the period of September 22, 2021 – February 17, 2022. In the results we found, R² exceeds, in general, 0.96 and, in most cases, they can reach 0.99. The humidity was found to be the less significant variable on the PMs concentrations, and the best accuracy was found by combining the pressure with the temperature. In addition, PM10 concentrations were found effectively related to the PM2.5 concentrations and with a bit strongly to the PM1. The PM10 concentration is not strongly correlated to the Noise and CO2 data. Finally, several new relationships have been built to forecast the PMs concentrations and the AQI based on the best combinations of predictor variables found.

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Section: Discussionmentioning

confidence: 99%

Air pollution forecasting and modelling using hybrid machine learning for Craiova City in Romania

Udriștioiu

MGHOUGHI

Yıldızhan

2022

Preprint

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“…The described FFNN-BP model is implemented to predict the daily global solar radiation in 25 different Moroccan cities (El Mghouchi et al, 2016;El Mghouchi et al, 2019). The FFNN-BP model operates with the monitoring of the learning approach and the Levenberg-Marquardt training algorithm, as illustrated by Al-Alawi and Al-Hinai (1998).…”

Section: Prediction Model Architecturementioning

confidence: 99%

Long-Term Global Solar Radiation Prediction in 25 Cities in Morocco Using the FFNN-BP Method

et al. 2021

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This article presents different combinations of input parameters based on an intelligent technique, using neural networks to predict daily global solar radiation (GSR) for twenty-five Moroccan cities. The collected measured data are available for 365 days and 25 stations around Morocco. Different input parameters are used, such as clearness index KT, day number, the length of the day, minimal temperature Tmin, maximal temperature Tmax, average temperature Taverage, difference temperature ΔT, ratio temperature T-Ratio, average relative humidity RH, solar radiation at the top outside atmosphere TOA, average wind speed Ws, altitude, longitude, latitude, and solar declination. A different combination was employed to predict daily GSR for the considered locations in order to find the most adequate input parameter that can be used in the prediction procedure. Several statistical metrics are applied to evaluate the performance of the obtained results, such as coefficients of determination (R2), mean absolute percentage error (MAPE), root mean square error (RMSE), normalized root mean square error (NRMSE), mean bias error (MBE), test statistic (TS), linear regression coefficients (the slope “a” and the constant “b”), and standard deviation (σ). It is found that the usage of input parameters gives highly accurate results in the artificial neural network (FFNN-BP) model, obtaining the lowest value of the statistical metrics. The results showed the best input of 25 locations, 12 inputs for Er-Rachidia, Marrakech, Medilt, Taza, Oujda, Nador, Tetouan, Tanger, Al-Auin, Dakhla, Settat, and Safi, seven inputs for Fes, Ifrane, Beni-Mellal, and Meknes, six inputs for Agadir and Rabat, five inputs for Sidi Ifni, Essaouira, Casablanca and Kenitra, four inputs for Ouarzazate, Lareche, and Al-Hoceima. In terms of accuracy, R2 of the selected best inputs parameters varies between 0.9860% and 0.9920%, the range value of MBE (%) being from −0.1076% to −0.5931%, the RMSE between 0.1990 and 0.4580%, the range value of the NRMSE between 0.0355 and 0.8938, and the lowest value MAPE between 0.0019 and 0.0060%. This technique could be used to predict other parameters for locations where measurement instrumentation is unavailable or costly to obtain.

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“…The accuracy of the forecasting technique is evaluated by the performance metrics of the root mean square error (RMSE), normal root mean square error (NRMSE), mean absolute error (MAE), Willmott's index of agreement (WIA) [41] and Legate's coefficient of efficiency (LCE) [41]. All performance metrics are shown in Equation (17) to Equation (21):…”

Section: Performance Metricsmentioning

confidence: 99%

Forecasting Solar Power Using Hybrid Firefly and Particle Swarm Optimization (HFPSO) for Optimizing the Parameters in a Wavelet Transform-Adaptive Neuro Fuzzy Inference System (WT-ANFIS)

et al. 2019

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Solar power generation deals with uncertainty and intermittency issues that lead to some difficulties in controlling the whole grid system due to imbalanced power production and power demand. The forecasting of solar power is an effort in securing the integration of renewable energy into the grid. This work proposes a forecasting model called WT-ANFIS-HFPSO which combines the wavelet transform (WT), adaptive neuro-fuzzy inference system (ANFIS) and hybrid firefly and particle swarm optimization algorithm (HFPSO). In the proposed work, the WT model is used to eliminate the noise in the meteorological data and solar power data whereby the ANFIS is functioning as the forecasting model of the hourly solar power data. The HFPSO is the hybridization of the firefly (FF) and particle swarm optimization (PSO) algorithm, which is employed in optimizing the premise parameters of the ANFIS to increase the accuracy of the model. The results obtained from WT-ANFIS-HFPSO are then compared with several other forecasting strategies. From the comparative analysis, the WT-ANFIS-HFPSO showed superior performance in terms of statistical error analysis, confirming its reliability as an excellent forecaster of hourly solar power data.

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Assessment of different combinations of meteorological parameters for predicting daily global solar radiation using artificial neural networks

Cited by 48 publications

References 43 publications

Air pollution forecasting and modelling using hybrid machine learning for Craiova City in Romania

Air pollution forecasting and modelling using hybrid machine learning for Craiova City in Romania

Long-Term Global Solar Radiation Prediction in 25 Cities in Morocco Using the FFNN-BP Method

Forecasting Solar Power Using Hybrid Firefly and Particle Swarm Optimization (HFPSO) for Optimizing the Parameters in a Wavelet Transform-Adaptive Neuro Fuzzy Inference System (WT-ANFIS)

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