Historical PV-output characteristic extraction based weather-type classification strategy and its forecasting method for the day-ahead prediction of PV output

Zheng, Lingwei; Su, Ran; Sun, Xinyu; Guo, Siqi

doi:10.1016/j.energy.2023.127009

Cited by 17 publications

(2 citation statements)

References 49 publications

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“…Seasonal climate change and human life rhythms are coupled in time, and the cyclicality of meteorology is related to agriculture or industrial production as well. Moreover, meteorological factors such as temperature, humidity, and precipitation, which come from chaotic systems [24][25][26], directly affect the life rhythm of residential electricity consumption. In cases of extreme heat and extreme cold, humans will use more electrical equipment to control the environmental temperature.…”

Section: External Features From Datetime and Meteorological Informati...mentioning

confidence: 99%

Extreme Gradient Boosting Model for Day-Ahead STLF in National Level Power System: Estonia Case Study

Zhao,

Liu,

Fang

2023

Energies

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Short-term power load forecasting refers to the use of load and weather information to forecast the Day-ahead load, which is very important for power dispatch and the establishment of the power spot market. In this manuscript, a comprehensive study on the frame of input data for electricity load forecasting is proposed based on the extreme gradient boosting algorithm. Periodicity was the first of the historical load data to be analyzed using discrete Fourier transform, autocorrelation function, and partial autocorrelation function to determine the key width of a sliding window for an optimization load feature. The mean absolute error (MAE) of the frame reached 52.04 using a boosting model with a 7-day width in the validation dataset. Second, the fusing of datetime variables and meteorological information factors was discussed in detail and determined how to best improve performance. The datetime variables were determined as a form of integer, sine–cosine pairs, and Boolean-type combinations, and the meteorological features were determined as a combination with 540 features from 15 sampled sites, which further decreased MAE to 44.32 in the validation dataset. Last, a training method for day-ahead forecasting was proposed to combine the Minkowski distance to determine the historical span. Under this framework, the performance has been significantly improved without any tuning for the boosting algorithm. The proposed method further decreased MAE to 37.84. Finally, the effectiveness of the proposed method is evaluated using a 200-day load dataset from the Estonian grid. The achieved MAE of 41.69 outperforms other baseline models, with MAE ranging from 65.03 to 104.05. This represents a significant improvement of 35.89% over the method currently employed by the European Network of Transmission System Operators for Electricity (ENTSO-E). The robustness of the proposal method can be also guaranteed with excellent performance in extreme weather and on special days.

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Section: External Features From Datetime and Meteorological Informati...mentioning

confidence: 99%

Extreme Gradient Boosting Model for Day-Ahead STLF in National Level Power System: Estonia Case Study

Zhao,

Liu,

Fang

2023

Energies

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“…Further more, it is found in previous research that adopting the SVM for weather classification with limited historical data has higher classification accuracy and robustness than using k-nearest neighbour (KNN) algorithm 13 . Zheng et al 14 deduced weather types by backward derivation of historical data and suggested that one of the ways to improve the accuracy of PV prediction in the future is to improve the temporal resolution of weather classification. The grey relation analysis (GRA) has expanded meteorological and historical PV output information 15 .…”

Section: Introductionmentioning

confidence: 99%

Small-sample short-term photovoltaic output prediction model based on GRA-SSA-GNNM method

Wang,

Mutailipu,

Jing

et al. 2024

Preprint

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The precision of photovoltaic (PV) output forecasting results is crucial to the reliability of the intelligent distribution network and multi-energy supplementary system. This work aims to address problems of insufficient research related to the short-term prediction of small-sample PV power generation and the low prediction accuracy in the previous research. A hybrid prediction model based on grey relation analysis (GRA) combined by sparrow search algorithm (SSA), and grey neural network model (GNNM) is proposed. In this paper, GRA is utilized to reduce the dimension of meteorological features of the samples. Then, a high-precision day-ahead short-term PV production forecast based on the SSA-GNNM model is established. The GNNM is used to perform regression analysis on the input features after reducing the dimension of meteorological features of the samples, and the parameters of the GNNM are optimised via SSA. The prediction results agrees well with the data from PV power plant in Xinjiang, indicating that the GRA-SSA-GNNM model developed in this work effectively achieves a high precision estimation in short-term PV power generation output prediction and has a promising application in this field.

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Research on energy storage capacity optimization of rural household photovoltaic system considering energy storage sharing

Wang,

Kang

2024

Environ Sci Pollut Res

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Historical PV-output characteristic extraction based weather-type classification strategy and its forecasting method for the day-ahead prediction of PV output

Cited by 17 publications

References 49 publications

Extreme Gradient Boosting Model for Day-Ahead STLF in National Level Power System: Estonia Case Study

Extreme Gradient Boosting Model for Day-Ahead STLF in National Level Power System: Estonia Case Study

Small-sample short-term photovoltaic output prediction model based on GRA-SSA-GNNM method

Research on energy storage capacity optimization of rural household photovoltaic system considering energy storage sharing

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