A hybrid ARIMA–ANN method to forecast daily global solar radiation in three different cities in Morocco

Belmahdi, Brahim; Louzazni, Mohamed; Bouardi, Abdelmajid El

doi:10.1140/epjp/s13360-020-00920-9

Cited by 25 publications

(6 citation statements)

References 62 publications

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“…As they found, higher forecast accuracy has been found by the proposed method (ARMA and GARCH) in comparison to other machine learning-based techniques. In addition, this model gave additional information about the uncertainty of the forecasts which was easier to set up [ 102 ].…”

Section: Ensemble Forecastingmentioning

confidence: 99%

A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

Rahimi

Park

Choi

et al. 2023

J. Electr. Eng. Technol.

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With increasing demand for energy, the penetration of alternative sources such as renewable energy in power grids has increased. Solar energy is one of the most common and well-known sources of energy in existing networks. But because of its non-stationary and non-linear characteristics, it needs to predict solar irradiance to provide more reliable Photovoltaic (PV) plants and manage the power of supply and demand. Although there are various methods to predict the solar irradiance. This paper gives the overview of recent studies with focus on solar irradiance forecasting with ensemble methods which are divided into two main categories: competitive and cooperative ensemble forecasting. In addition, parameter diversity and data diversity are considered as competitive ensemble forecasting and also preprocessing and post-processing are as cooperative ensemble forecasting. All these ensemble forecasting methods are investigated in this study. In the end, the conclusion has been drawn and the recommendations for future studies have been discussed.

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Section: Ensemble Forecastingmentioning

confidence: 99%

A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

Rahimi

Park

Choi

et al. 2023

J. Electr. Eng. Technol.

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“…With a larger forecasting term, the daily GHI is predicted using ANN models for 25 Moroccan cities in [31], with empirical and machine learning models for 5 Moroccan cities in [32] and with hybrid ARIMA-ANN model for 3 cities in Morocco in [33]. The daily GHI is also forecasted with ANN models for 35 Moroccan, Algerian, Spanish and Mauritian cities in [34] and the monthly mean daily GHI using time series models in [35].…”

Section: State Of the Artmentioning

confidence: 99%

“…[9] Intra-hour GHI cloud retrieval technique to develop a physics-based smart persistence model [10] Intra-day GHI and DNI algorithm using cloud physical properties [11] A 15 min GHI forecasting model [12] Hourly-averaged GHI forecasts [13] Hourly GHI and DNI clear-sky irradiance vs. RRTMG physical radiative transfer model [14] Hourly and daily GHI from mesoscale atmospheric weather research forecasting model [15] Hourly GHI with a three-dimensional meteorology-chemistry model including a treatment of aerosols [16] Hourly GHI exponential smoothing model with decomposition methods [17] A 1 min DNI under a probabilistic approach [18] Short-term GHI with hybrid convolutional ANN model with spatiotemporal correlations [19] Short-term GHI and DNI forecasts of a global numerical weather model [20,21] A 5-30 min GHI and DNI with machine learning techniques [22] A 15 min GHI and DNI with machine learning techniques [23] Intra-day GHI with machine learning techniques [24] A 30 min GHI with ANN algorithm [25,26] Hourly GHI ANN models [27] Mean daily GHI with ANN models [28] A 500 ms-5 min GHI based on k-means algorithm [29] A 5-30 min GHI and DNI based on the k-nearest neighbours algorithm [30] A 30 min-5 h GHI Gaussian process regression method [31] Daily GHI with ANN models for for 25 Moroccan cities [32] Daily GHI with empirical and machine learning models for 5 Moroccan cities [33] Monthly mean daily GHI using time series models [34] Daily GHI with hybrid ARIMA-ANN model for 3 cities in Morocco [35] Daily GHI with ANN models for 35 Moroccan, Algerian, Spanish and Mauritian cities [36] Best Practices Handbook for the Collection and Use of Solar Resource Data, selection of potential sites [37] Steps for solar resource assessment, selection of potential sites [38] Solar resource assessment, selection of potential sites [39] Monthly data, ANN models are used to estimate it in Saudi Arabia …”

Section: Short-term Irradiance Forecasting [9-35]mentioning

confidence: 99%

A Comprehensive Methodology for the Statistical Characterization of Solar Irradiation: Application to the Case of Morocco

et al. 2023

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The prediction and characterization of solar irradiation relies mostly on either the use of complex models or on complicated mathematical techniques, such as artificial neural network (ANN)-based algorithms. This mathematical complexity might hamper their use by businesses and project developers when assessing the solar resource. In this study, a simple but comprehensive methodology for characterizing the solar resource for a project is presented. It is based on the determination of the best probability distribution function (PDF) of the solar irradiation for a specific location, assuming that the knowledge of statistical techniques may be more widely extended than other more complex mathematical methods. The presented methodology was tested on 23 cities across Morocco, given the high interest in solar investments in the country. As a result, a new database for solar irradiation values depending on historical data is provided for Morocco. The results show the great existing variety of PDFs for the solar irradiation data at the different months and cities, which demonstrates the need for undertaking a proper characterization of the irradiation when the assessment of solar energy projects is involved. When it is simply needed to embed the radiation uncertainty in the analysis, as is the case of the techno-economic valuation of solar energy assets, the presented methodology can reach this objective with much less complexity and less demanding input data. Moreover, its application is not limited to solar resource assessment, but can also be easily used in other fields, such as meteorology and climate change studies.

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“…Physical prediction techniques and time series prediction techniques are the two main traditional methodologies. The early stages of PV power prediction have seen the extensive use of the time series prediction method, but it has lower prediction accuracy [10,11]. Traditional machine learning models, such as random forest (RF) [12] and support vector machine (SVM) [13,14], which have higher prediction accuracy, are primarily used in applications of artificial intelligence algorithms.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Prediction of PV Power Based on Combined Modal Decomposition and NARX-LSTM-LightGBM

et al. 2023

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Recently, solar energy has been gaining attention as one of the best promising renewable energy sources. Accurate PV power prediction models can solve the impact on the power system due to the non-linearity and randomness of PV power generation and play a crucial role in the operation and scheduling of power plants. This paper proposes a novel machine learning network framework to predict short-term PV power in a time-series manner. The combination of nonlinear auto-regressive neural networks with exogenous input (NARX), long short term memory (LSTM) neural network, and light gradient boosting machine (LightGBM) prediction model (NARX-LSTM-LightGBM) was constructed based on the combined modal decomposition. Specifically, this paper uses a dataset that includes ambient temperature, irradiance, inverter temperature, module temperature, etc. Firstly, the feature variables with high correlation effects on PV power were selected by Pearson correlation analysis. Furthermore, the PV power is decomposed into a new feature matrix by (EMD), (EEMD) and (CEEMDAN), i.e., the combination decomposition (CD), which deeply explores the intrinsic connection of PV power historical series information and reduces the non-smoothness of PV power. Finally, preliminary PV power prediction values and error correction vector are obtained by NARX prediction. Both are embedded into the NARX-LSTM-LightGBM model pair for PV power prediction, and then the error inverse method is used for weighted optimization to improve the accuracy of the PV power prediction. The experiments were conducted with the measured data from Andre Agassi College, USA, and the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the model under different weather conditions were lower than 1.665 kw, 0.892 kw and 0.211, respectively, which are better than the prediction results of other models and proved the effectiveness of the model.

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A hybrid ARIMA–ANN method to forecast daily global solar radiation in three different cities in Morocco

Cited by 25 publications

References 62 publications

A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

A Comprehensive Methodology for the Statistical Characterization of Solar Irradiation: Application to the Case of Morocco

Short-Term Prediction of PV Power Based on Combined Modal Decomposition and NARX-LSTM-LightGBM

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