Hybrid Forecasting Methodology for Wind Power-Photovoltaic-Concentrating Solar Power Generation Clustered Renewable Energy Systems

Pang, Simian; Zheng, Zixuan; Luo, Fan; Xiao, Xianyong; Xu, Longhua

doi:10.3390/su13126681

Cited by 13 publications

(2 citation statements)

References 34 publications

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“…As the energy emergency intensifies, sustainable power sources such as wind and solar energy have gained the limelight. Global wind power generation reached 733 GW in 2020, an increase of 17.8% over 2019 [4]. Solar power generation reached 714 GW in 2020, an approximate increase of 21.6% from the previous year [5,6].…”

Section: Introductionmentioning

confidence: 99%

Management of Distributed Renewable Energy Resources with the Help of a Wireless Sensor Network

2022

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Photovoltaic (PV) and wind energy are widely considered eco-friendly renewable energy resources. However, due to the unpredictable oscillations in solar and wind power production, efficient management to meet load demands is often hard to achieve. As a result, precise forecasting of PV and wind energy production is critical for grid managers to limit the impact of random fluctuations. In this study, the kernel recursive least-squares (KRLS) algorithm is proposed for the prediction of PV and wind energy. The wireless sensor network (WSN) typically adopted for data collection with a flexible configuration of sensor nodes is used to transport PV and wind production data to the monitoring center. For efficient transmission of the data production, a link scheduling technique based on sensor node attributes is proposed. Different statistical and machine learning (ML) techniques are examined with respect to the proposed KRLS algorithm for performance analysis. The comparison results show that the KRLS algorithm surpasses all other regression approaches. For both PV and wind power feed-in forecasts, the proposed KRLS algorithm demonstrates high forecasting accuracy. In addition, the link scheduling proposed for the transmission of data for the management of distributed renewable energy resources is compared with a reference technique to show its comparable performance. The efficacy of the proposed KRLS model is better than other regression models in all assessment events in terms of an RMSE value of 0.0146, MAE value of 0.00021, and R2 of 99.7% for PV power, and RMSE value of 0.0421, MAE value of 0.0018, and R2 of 88.17% for wind power. In addition to this, the proposed link scheduling approach results in 22% lower latency and 38% higher resource utilization through the efficient scheduling of time slots.

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Section: Introductionmentioning

confidence: 99%

Management of Distributed Renewable Energy Resources with the Help of a Wireless Sensor Network

2022

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“…In this work, the ANN structure was applied to forecast PV output by means of irradiation and temperature data extracted from Global Data Assimilation System (GDAS). In a recent work [34], a short-term prediction system was presented to improve the hybrid forecasting accuracy of multiple generation sources, such as PV and wind in the same area. The researchers in [35] proposed a two-step method for PV power forecasting using weather data through Machine Learning.…”

Section: Introductionmentioning

confidence: 99%

An Improved Approach to Enhance Training Performance of ANN and the Prediction of PV Power for Any Time-Span without the Presence of Real-Time Weather Data

et al. 2021

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In this work, an improved approach to enhance the training performance of an Artificial Neural Network (ANN) for prediction of the output of renewable energy systems is proposed. Using the proposed approach, a significant reduction of the Mean Squared Error (MSE) in training performance is achieved, specifically from 4.45 × 10−7 to 3.19 × 10−10. Moreover, a simplified application of the already trained ANN is introduced through which photovoltaic (PV) output can be predicted without the availability of real-time current weather data. Moreover, unlike the existing prediction models, which ask the user to apply multiple inputs in order to forecast power, the proposed model requires only the set of dates specifying forecasting period as the input for prediction purposes. Moreover, in the presence of the historical weather data this model is able to predict PV power for different time spans rather than only for a fixed period. The prediction accuracy of the proposed model has been validated by comparing the predicted power values with the actual ones under different weather conditions. To calculate actual power, the data were obtained from the National Renewable Energy Laboratory (NREL), USA and from the Universiti Teknologi Malaysia (UTM), Malaysia. It is envisaged that the proposed model can be easily handled by a non-technical user to assess the feasibility of the photovoltaic solar energy system before its installation.

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Intelligent Whale Algorithm for the Design of Multi-Utility Renewable Energy Hybrid Plants

Meng,

Ye,

Huang

2024

J. Electr. Eng. Technol.

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Hybrid Forecasting Methodology for Wind Power-Photovoltaic-Concentrating Solar Power Generation Clustered Renewable Energy Systems

Cited by 13 publications

References 34 publications

Management of Distributed Renewable Energy Resources with the Help of a Wireless Sensor Network

Management of Distributed Renewable Energy Resources with the Help of a Wireless Sensor Network

An Improved Approach to Enhance Training Performance of ANN and the Prediction of PV Power for Any Time-Span without the Presence of Real-Time Weather Data

Intelligent Whale Algorithm for the Design of Multi-Utility Renewable Energy Hybrid Plants

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