Application of extreme learning machine for short term output power forecasting of three grid-connected PV systems

Hossain, M.S.; Mekhilef, Saad; Danesh, Malihe; Olatomiwa, Lanre; Shamshirband, Shahaboddin

doi:10.1016/j.jclepro.2017.08.081

Cited by 227 publications

(93 citation statements)

References 43 publications

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“…The normalized mean square error (NMSE) is determined as the overall differences between the predicted and desired values, which computed as in Equation (28). The normalized mean square error (NMSE) is determined as the overall differences between the predicted and desired values, which computed as in Equation (28).…”

Section: Deep Learning Methodsmentioning

confidence: 99%

“…The simulation results in this study proved that the use of this concept can achieve the desired goal and is very accurate. Hossain et al 28 was concerned with building a model that predicts the output of a PV system and adopted in building this model on the extreme learning machine (ELM). The results of the proposed model were compared with the results of other studies that adopted different models, such as the support vector regression (SVR) and ANN.…”

Section: Introductionmentioning

confidence: 99%

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Experimental and deep learning artificial neural network approach for evaluating grid-connected photovoltaic systems

et al. 2019

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Summary This article evaluates a 1.4‐kW building integrated grid‐connected photovoltaic plant. The PV plant was installed in the Faculty of Engineering solar energy lab, Sohar University, Oman, and the system data have been collected for a year from July 2017 to June 2018. The grid‐connected system was evaluated in terms of power, energy, specific yield, capacity factor, and cost of energy, and payback period. The measured diffuse and global solar irradiations are 3289 and 6182 Wh/m2, respectively. Four predictive models (TLRN, FRNN‐1, FRNN‐2, and FRNN‐3) using deep learning approach based on RNN and TLRN were proposed to predict the PV current performance through data input of temperature (T) and solar irradiance (G). The experiment results found that the highest energy production, array, reference, and final yields are 245.8 kWh, 3.43 to 5.65 kWh/kWp‐day, 4.61 to 7.33 kWh/kWp‐day, and 3.24 to 4.82 kWh/kWp‐day, respectively. Meanwhile, CF, CoE, and PBP were found to be 21.7%, 0.045 USD/kWh and 11.17 years, respectively. The highest performance for prediction models were found for FRNN‐2 and FRNN‐3 due to they exhibit lower MSE which means being tightly fitted to experiments.

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Section: Deep Learning Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Experimental and deep learning artificial neural network approach for evaluating grid-connected photovoltaic systems

et al. 2019

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“…These have been mainstream methods in the time series forecasting field. The main methods of time series prediction based on machine learning are Support Vector Machines (SVMs), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machines (LightGBMs), and deep neural networks [12][13][14][15]. When the sample of the data set is large, the calculation speed of the SVM will become very slow.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Photovoltaic Power Output Prediction Based on k-Fold Cross-Validation and an Ensemble Model

2019

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Short-term photovoltaic power forecasting is of great significance for improving the operation of power systems and increasing the penetration of photovoltaic power. To improve the accuracy of short-term photovoltaic power forecasting, an ensemble-model-based short-term photovoltaic power prediction method is proposed. Firstly, the quartile method is used to process raw data, and the Pearson coefficient method is utilized to assess multiple features affecting the short-term photovoltaic power output. Secondly, the structure of the ensemble model is constructed, and a k-fold cross-validation method is used to train the submodels. The prediction results of each submodel are merged. Finally, the validity of the proposed approach is verified using an actual data set from State Power Investment Corporation Limited. The simulation results show that the quartile method can find outliers which contributes to processing the raw data and improving the accuracy of the model. The k-fold cross-validation method can effectively improve the generalization ability of the model, and the ensemble model can achieve higher prediction accuracy than a single model.

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“…The results illustrated that the model achieved reliable forecasting performance that can be utilized in various applications. [15] developed a short-term forecasting model based on extreme machine learning method for three grid connected PV systems. The proposed model is claimed to support integrating PV plants into power systems and that it is important for grid stability issues, economic dispatch, and regulations.…”

Section: Introductionmentioning

confidence: 99%

PVPF tool: an automatedWeb application for real-time photovoltaic power forecasting

Alomari¹,

Adeeb²,

Younis³

2019

IJECE

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In this paper, we propose a fully automated machine learning based forecasting system, called Photovoltaic Power Forecasting (PVPF) tool, that applies optimised neural networks algorithms to real-time weather data to provide 24 hours ahead forecasts for the power production of solar photovoltaic systems installed within the same region. This system imports the real-time temperature and global solar irradiance records from the ASU weather station and associates these records with the available solar PV production measurements to provide the proper inputs for the pre-trained machine learning system along with the records' time with respect to the current year. The machine learning system was pre-trained and optimised based on the Bayesian Regularization (BR) algorithm, as described in our previous research, and used to predict the solar power PV production for the next 24 hours using weather data of the last five consecutive days. Hourly predictions are provided as a power/time curve and published in real-time at the website of the renewable energy center (REC) of Applied Science Private University (ASU). It is believed that the forecasts provided by the PVPF tool can be helpful for energy management and control systems and will be used widely for the future research activities at REC.

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Application of extreme learning machine for short term output power forecasting of three grid-connected PV systems

Cited by 227 publications

References 43 publications

Experimental and deep learning artificial neural network approach for evaluating grid-connected photovoltaic systems

Experimental and deep learning artificial neural network approach for evaluating grid-connected photovoltaic systems

Short-Term Photovoltaic Power Output Prediction Based on k-Fold Cross-Validation and an Ensemble Model

PVPF tool: an automatedWeb application for real-time photovoltaic power forecasting

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