Correlation Feature Selection and Mutual Information Theory Based Quantitative Research on Meteorological Impact Factors of Module Temperature for Solar Photovoltaic Systems

Sun, Yujing; Wang, Fei; Wang, Bo; Chen, Qifang; Engerer, Nicholas A.; Mi, Zengqiang

doi:10.3390/en10010007

Cited by 50 publications

(18 citation statements)

References 33 publications

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“…Also, two-thirds of the area of China is rich in solar energy sources. Theoretically, potential sources add up to 1.7 trillion tons of coal equivalent each year [5,53].…”

Section: Solar Energymentioning

confidence: 99%

Consumers’ Attitudes to Support Green Energy: A Case Study in Shanghai

et al. 2019

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Residents’ willingness to use green energy products is a major concern for different stakeholders and policymakers due to the reformed Chinese electricity market since 2015. This study focused on the Chinese consumer’s willingness to opt and pay for environmentally-friendly electricity sources in Shanghai’s residential sector. A questionnaire survey was used to find out the influence of gender, age, education, awareness and income level on consumers’ attitudes to alter their energy sources to green ones. The results indicated that income level and awareness are significant barriers in the usage of green energy products. Increasing the respondent’s awareness about the issues of non-green energy products convinced 97% of them to change their electricity sources completely or partly, in line with their monthly income. This clearly shows that clarifying the benefits of green energy products is a key tool to achieve a green environment in China.

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“…Also, two-thirds of the area of China is rich in solar energy sources. Theoretically, potential sources add up to 1.7 trillion tons of coal equivalent each year [5,53].…”

Section: Solar Energymentioning

confidence: 99%

Consumers’ Attitudes to Support Green Energy: A Case Study in Shanghai

et al. 2019

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“…• Statistical learning based methods-work best for the intra-hour forecast horizons, but can also be applied for longer forecasting, up to 2 or 3 h, when combined with other methods [25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

et al. 2017

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Accurate solar photovoltaic (PV) power forecasting is an essential tool for mitigating the negative effects caused by the uncertainty of PV output power in systems with high penetration levels of solar PV generation. Weather classification based modeling is an effective way to increase the accuracy of day-ahead short-term (DAST) solar PV power forecasting because PV output power is strongly dependent on the specific weather conditions in a given time period. However, the accuracy of daily weather classification relies on both the applied classifiers and the training data. This paper aims to reveal how these two factors impact the classification performance and to delineate the relation between classification accuracy and sample dataset scale. Two commonly used classification methods, K-nearest neighbors (KNN) and support vector machines (SVM) are applied to classify the daily local weather types for DAST solar PV power forecasting using the operation data from a grid-connected PV plant in Hohhot, Inner Mongolia, China. We assessed the performance of SVM and KNN approaches, and then investigated the influences of sample scale, the number of categories, and the data distribution in different categories on the daily weather classification results. The simulation results illustrate that SVM performs well with small sample scale, while KNN is more sensitive to the length of the training dataset and can achieve higher accuracy than SVM with sufficient samples.

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“…Several different models have been successfully applied to the forecasting of renewable power generation in terms of wind power [19][20][21][22][23] and solar power [24][25][26]. In this paper, the ARMA time series model is applied to establish the forecasting model based on historical data [21].…”

Section: Arma-based Power Forecasting Modelmentioning

confidence: 99%

Two-Tier Reactive Power and Voltage Control Strategy Based on ARMA Renewable Power Forecasting Models

Wang

Ren

et al. 2017

Energies

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Abstract:To address the static voltage stability issue and suppress the voltage fluctuation caused by the increasing integration of wind farms and solar photovoltaic (PV) power plants, a two-tier reactive power and voltage control strategy based on ARMA power forecasting models for wind and solar plants is proposed in this paper. Firstly, ARMA models are established to forecast the output of wind farms and solar PV plants. Secondly, the discrete equipment is pre-regulated based on the single-step prediction information from ARMA forecasting models according to the optimization result. Thirdly, a multi-objective optimization model is presented and solved by particle swarm optimization (PSO) according to the measured data and the proposed static voltage stability index. Finally, the IEEE14 bus system including a wind farm and solar PV plant is utilized to test the effectiveness of the proposed strategy. The results show that the proposed strategy can suppress voltage fluctuation and improve the static voltage stability under the condition of high penetration of renewables including wind and solar power.

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Correlation Feature Selection and Mutual Information Theory Based Quantitative Research on Meteorological Impact Factors of Module Temperature for Solar Photovoltaic Systems

Cited by 50 publications

References 33 publications

Consumers’ Attitudes to Support Green Energy: A Case Study in Shanghai

Consumers’ Attitudes to Support Green Energy: A Case Study in Shanghai

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

Two-Tier Reactive Power and Voltage Control Strategy Based on ARMA Renewable Power Forecasting Models

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