Adaptive ML-based technique for renewable energy system power forecasting in hybrid PV-Wind farms power conversion systems

Zafar, Muhammad Usman; Khan, Noman Mujeeb; Mansoor, Majad; Mirza, Adeel Feroz; Moosavi, Syed Kumayl Raza; Sanfilippo, Filippo

doi:10.1016/j.enconman.2022.115564

Cited by 27 publications

(6 citation statements)

References 34 publications

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“…In [37], a modified butterfly optimization (MBO) position updating mechanism was improved by utilizing the exploitation capabilities of the multi-verse optimizer (MVO). The work in [38] combined the exploration capabilities of a sine cosine algorithm (SCA) with a dynamic group-based cooperative optimization algorithm (DGCO) for efficient training of a radial basis function neural network. In [39], the performance of a salp swarm algorithm was greatly improved by integrating Levy flight and sine cosine operators.…”

Section: Literature Surveymentioning

confidence: 99%

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Improved Reptile Search Optimization Algorithm: Application on Regression and Classification Problems

et al. 2023

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The reptile search algorithm is a newly developed optimization technique that can efficiently solve various optimization problems. However, while solving high-dimensional nonconvex optimization problems, the reptile search algorithm retains some drawbacks, such as slow convergence speed, high computational complexity, and local minima trapping. Therefore, an improved reptile search algorithm (IRSA) based on a sine cosine algorithm and Levy flight is proposed in this work. The modified sine cosine algorithm with enhanced global search capabilities avoids local minima trapping by conducting a full-scale search of the solution space, and the Levy flight operator with a jump size control factor increases the exploitation capabilities of the search agents. The enhanced algorithm was applied to a set of 23 well-known test functions. Additionally, statistical analysis was performed by considering 30 runs for various performance measures like best, worse, average values, and standard deviation. The statistical results showed that the improved reptile search algorithm gives a fast convergence speed, low time complexity, and efficient global search. For further verification, improved reptile search algorithm results were compared with the RSA and various state-of-the-art metaheuristic techniques. In the second phase of the paper, we used the IRSA to train hyperparameters such as weight and biases for a multi-layer perceptron neural network and a smoothing parameter (σ) for a radial basis function neural network. To validate the effectiveness of training, the improved reptile search algorithm trained multi-layer perceptron neural network classifier was tested on various challenging, real-world classification problems. Furthermore, as a second application, the IRSA-trained RBFNN regression model was used for day-ahead wind and solar power forecasting. Experimental results clearly demonstrated the superior classification and prediction capabilities of the proposed hybrid model. Qualitative, quantitative, comparative, statistical, and complexity analysis revealed improved global exploration, high efficiency, high convergence speed, high prediction accuracy, and low time complexity in the proposed technique.

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Section: Literature Surveymentioning

confidence: 99%

“…In order to compare the predictive capabilities of the selected models, we used several statistical measures, such as root mean square error (RMSE), relative error (RE), and the coefficient of determination (R 2 ) [38]. These indicators are described by the following equations:…”

Section: Statistical Indicators For Regressionmentioning

confidence: 99%

Improved Reptile Search Optimization Algorithm: Application on Regression and Classification Problems

et al. 2023

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“…When the WSPC is used to predict at a certain point, the measured WS cannot be obtained, so it is necessary to correct the WS of NWP to improve the prediction accuracy. In this paper, the WS correction model proposed in the literature [28] is adopted, and the LSTM is used for modeling. Te WS correction strategy is shown in Figure 2.…”

Section: Te Dynamic Switching Mechanism Of Multiple Wppmentioning

confidence: 99%

An Ultrashort-Term Wind Power Prediction Method Based on a Switching Output Mechanism

Feng

Wang

et al. 2023

International Transactions on Electrical Energy Systems

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The ultrashort-term wind power prediction (USTWPP) technology assists the grid to arrange spare capacity, which is important to optimize power investment reasonably. To improve the accuracy of USTWPP and optimize power investment requirements, a USTWPP method with dynamic switching of multiple models is proposed. For high wind speed fluctuation samples, the wind speed-power curve (WSPC) is fitted in a large sample of historical data, and the corrected wind speed is the input of WSPC. The spatiotemporal attentive network model (STAN) is built for the prediction of low wind speed fluctuation samples. According to the real-time fluctuation characteristics of the correction wind speed, a switching mechanism between multiple models is established to reconstruct the prediction results along the time axis direction, and the predicted power is set to zero for the samples whose correction wind speed is lower than the cut-in wind speed. We conducted simulation experiments with data provided by a wind farm with an installed capacity of 130.5 MW in China. The normalized root mean square error (NRMSE) for the 4 h ahead predicted power reaches 0.0907, which verified the validity and applicability of the proposed model.

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“…Despite implementing DR or designing an energy storage system, an accurate forecasting model for renewable energy generation is crucial to optimize the power system and allow more renewable energies to penetrate into the grid 5 . Without accurate and reliable forecasting of renewable energy generation, the maximum benefits from the energy management system cannot be realized.…”

Section: Background and Summarymentioning

confidence: 99%

Solar and wind power data from the Chinese State Grid Renewable Energy Generation Forecasting Competition

Chen

2022

Sci Data

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Accurate solar and wind generation forecasting along with high renewable energy penetration in power grids throughout the world are crucial to the days-ahead power scheduling of energy systems. It is difficult to precisely forecast on-site power generation due to the intermittency and fluctuation characteristics of solar and wind energy. Solar and wind generation data from on-site sources are beneficial for the development of data-driven forecasting models. In this paper, an open dataset consisting of data collected from on-site renewable energy stations, including six wind farms and eight solar stations in China, is provided. Over two years (2019–2020), power generation and weather-related data were collected at 15-minute intervals. The dataset was used in the Renewable Energy Generation Forecasting Competition hosted by the Chinese State Grid in 2021. The process of data collection, data processing, and potential applications are described. The use of this dataset is promising for the development of data-driven forecasting models for renewable energy generation and the optimization of electricity demand response (DR) programs for the power grid.

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Adaptive ML-based technique for renewable energy system power forecasting in hybrid PV-Wind farms power conversion systems

Cited by 27 publications

References 34 publications

Improved Reptile Search Optimization Algorithm: Application on Regression and Classification Problems

Improved Reptile Search Optimization Algorithm: Application on Regression and Classification Problems

An Ultrashort-Term Wind Power Prediction Method Based on a Switching Output Mechanism

Solar and wind power data from the Chinese State Grid Renewable Energy Generation Forecasting Competition

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