Wind Speed and Solar Irradiance Prediction Using a Bidirectional Long Short-Term Memory Model Based on Neural Networks

Alharbi, Fahad Radhi; Csala, Dénes

doi:10.3390/en14206501

Cited by 23 publications

(16 citation statements)

References 49 publications

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“…The region witnessed a rapid movement with the launch of renewable energy projects, especially photovoltaic energy. For instance, the KSA constructed a 300 MW PV project in Sakaka province in 2019 (Alnaser et al, 2022), (Alharbi and Csala, 2021).…”

Section: Pv Solar Projects In the Gulf Cooperation Council Regionmentioning

confidence: 99%

Comprehensive economic analysis of PV farm -A case study of Alkarsaah PV farm in qatar

et al. 2022

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Countries around the world are rapidly deploying renewable energy generation to reduce carbon emissions. Countries in the Gulf Cooperation Council (GCC) are investing heavily in PV generation due to their rich solar resources. As PV technology becomes more mature, future PV developments will largely depend on the cost of the PV generation but there is currently very limited published work that shows a detailed design and in particular the economic analysis of large-scale PV farms. Therefore, this paper uses the Qatar’s first PV farm, the 800MWp Alkarsaah PV farm as a case study to explain the design considerations and especially the economic benefits of large-scale PV farms. Economic comparisons will be made with the most efficient CCGT (combined cycle gas turbine) plants in the network to highlight the economic benefits of PV farms. The results show that the Levelized cost of electricity (LCOE) for this PV farm is 14.03$/MWh, much lower than the LCOE of 39.18$/MWh and 24.6$/MWh from the most efficient CCGTs in the network, highlighting the significant economic benefits of developing PV farms in a low carbon power networks in the future.

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Section: Pv Solar Projects In the Gulf Cooperation Council Regionmentioning

confidence: 99%

Comprehensive economic analysis of PV farm -A case study of Alkarsaah PV farm in qatar

et al. 2022

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“…Hence, to increase the accuracy of the prediction model, hybridization of these DL models was also successfully carried out by different researchers for different applications, for example, CNN-LSTM [19] (short-term load forecasting model), LSTM-RNN [20] (shortterm solar forecasting), etc. The output results corresponding to these hybrid models shows an incremental accuracy level compared to solo techniques.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique

et al. 2022

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Variability in solar irradiance has an impact on the stability of solar systems and the grid’s safety. With the decreasing cost of solar panels and recent advancements in energy conversion technology, precise solar energy forecasting is critical for energy system integration. Despite extensive research, there is still potential for advancement of solar irradiance prediction accuracy, especially global horizontal irradiance. Global Horizontal Irradiance (GHI) (unit: KWh/m2) and the Plane Of Array (POA) irradiance (unit: W/m2) were used as the forecasting objectives in this research, and a hybrid short-term solar irradiance prediction model called modified multi-step Convolutional Neural Network (CNN)-stacked Long-Short-Term-Memory network (LSTM) with drop-out was proposed. The real solar data from Sweihan Photovoltaic Independent Power Project in Abu Dhabi, UAE is preprocessed, and features were extracted using modified CNN layers. The output result from CNN is used to predict the targets using a stacked LSTM network and the efficiency is proved by comparing statistical performance measures in terms of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MAE), and R2 scores, with other contemporary machine learning and deep-learning-based models. The proposed model offered the best RMSE and R2 values of 0.36 and 0.98 for solar irradiance prediction and 61.24 with R2 0.96 for POA prediction, which also showed better performance as compared to the published works in the literature.

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“…The two main types of deep learning approaches used in forecasting models are recurrent neural networks [3,4] and convolutional neural networks [5,6]. ANN approaches are the foundation of artificial intelligence because they tackle issues that are hard to address using computational criteria.…”

Section: Introductionmentioning

confidence: 99%

“…If p > 0.05, the mean time series contains a unit root, the null hypothesis is not rejected, and the data are non-stationary [6,11].…”

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confidence: 99%

A Seasonal Autoregressive Integrated Moving Average with Exogenous Factors (SARIMAX) Forecasting Model-Based Time Series Approach

Alharbi

Csala

2022

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Time series modeling is an effective approach for studying and analyzing the future performance of the power sector based on historical data. This study proposes a forecasting framework that applies a seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) model to forecast the long-term performance of the electricity sector (electricity consumption, generation, peak load, and installed capacity). In this study, the model was used to forecast the aforementioned factors in Saudi Arabia for 30 years from 2021 to 2050. The historical data that were inputted into the model were collected from Saudi Arabia at quarterly intervals across a 40-year period (1980−2020). The SARIMAX technique applies a time series approach with seasonal and exogenous influencing factors, which helps reduce the error values and improve the overall model accuracy, even in the case of close input and output dataset lengths. The experimental findings indicated that the SARIMAX model has promising performance in terms of categorization and consideration, as it has significantly improved forecasting accuracy compared with the simpler autoregressive integrated moving average-based techniques. Furthermore, the model is capable of coping with different-sized sequential datasets. Finally, the model aims to help address the issue of a lack of future planning and analyses of power performance and intermittency, and it provides a reliable forecasting technique, which is a prerequisite for modern energy systems.

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Wind Speed and Solar Irradiance Prediction Using a Bidirectional Long Short-Term Memory Model Based on Neural Networks

Cited by 23 publications

References 49 publications

Comprehensive economic analysis of PV farm -A case study of Alkarsaah PV farm in qatar

Comprehensive economic analysis of PV farm -A case study of Alkarsaah PV farm in qatar

Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique

A Seasonal Autoregressive Integrated Moving Average with Exogenous Factors (SARIMAX) Forecasting Model-Based Time Series Approach

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