Short-Term Photovoltaic Power Generation Combination Forecasting Method Based on Similar Day and Cross Entropy Theory

Wang, Qi; Ji, Shunxiang; Hu, Minqiang; Li, Wei; Liu, Fusuo; Zhu, Ling

doi:10.1155/2018/6973297

Cited by 22 publications

(8 citation statements)

References 13 publications

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“…The photovoltaic power sequence itself has chaotic characteristics (Du et al 2019), (Wang et al 2018) and its highly autocorrelated fluctuation characteristics imply various external factors. The chaotic phase space reconstruction is performed on it, and the information of the influencing factors contained in it can be recovered through the chaotic attractor trajectory, which lays the foundation for ultra-short-term prediction of photovoltaic power generation.…”

Section: Humiditymentioning

confidence: 99%

An Efficient Algorithm for Power Prediction in PV Generation System

Alsafasfeh

2020

Int. J. Renew. Energy Dev.

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Aiming at the existing photovoltaic power generation prediction methods, the modeling is complicated, the prediction accuracy is low, and it is difficult to meet the actual needs. Based on the improvement of the traditional wavelet neural network, a dual-mode cuckoo search wavelet neural network algorithm combined prediction method is proposed, which takes into account the extraction of chaotic features of surface solar radiation and photovoltaic output power. The proposed algorithm first reconstructs the chaotic phase space of the hidden information of each influencing factor in the data history of PV generation and according to the correlation analysis, the solar radiation is utilized as additional input. Next, the proposed algorithm overcomes the limitations of the cuckoo search algorithm such as the sensitivity to the initial value and searchability and convergence speed by dual-mode cuckoo search wavelet neural network algorithm. Lastly, a prediction model of the proposed algorithm is proposed and the prediction analysis is performed under different weather conditions. Simulation results show that the proposed algorithm shows better performance than the existing algorithms under different weather conditions. Under various weather conditions, the mean values of TIC, EMAE and ENRMSE error indicators of the proposed forecasting algorithm were reduced by 43.70%, 45.75%, and 45.41%, respectively. Compared with the Chaos-WNN prediction method, the prediction performance has been further improved under various weather conditions and the mean values of TIC, EMAE and ENRMSE error indicators have been reduced by 25.55%, 27.26%, and 36.83%, respectively. ©2020. CBIORE-IJRED. All rights reserved

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Section: Humiditymentioning

confidence: 99%

An Efficient Algorithm for Power Prediction in PV Generation System

Alsafasfeh

2020

Int. J. Renew. Energy Dev.

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“…Grey relation entropy is a combination of GRA and IE, which can be used to quantify the similarity between different sequences [18]. In this chapter, grey relational entropy is used to select the historical days with high similarity to the forecast day in terms of meteorological characteristics.…”

Section: Sda Based On Grey Relation Entropymentioning

confidence: 99%

“…The results show that the average daily RMSE, MAPE and R 2 of the model are 4.3210 kW, 2.8366% and 0.9953, respectively. In [18], a short‐term combination forecasting model for PV power based on similar days and cross entropy theory is proposed. Compared with combination models based on the sum of squared errors and correlation coefficient, the model has lower prediction error and better prediction performance.…”

Section: Introductionmentioning

confidence: 99%

A hybrid prediction model for photovoltaic power generation based on information entropy

Wan

et al. 2020

IET Generation Trans & Dist

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Photovoltaic power is affected by various random and coupled meteorological factors, and its changing trend implies the non-linear effects of these factors. According to the quantitative analysis results, a statistical prediction model is proposed to accurately predict the power, which is of great significance to the safe and efficient use of solar energy. In this study, the authors first use grey relation analysis to select four main meteorological factors affecting photovoltaic power. Further, they combine grey relation analysis with information entropy and apply grey relation entropy to similar day analysis. On this basis, they take grey relation analysis to optimise extreme learning machine model to establish the grey relation analysis-extreme learning machine model, while taking similar day analysis to optimise firefly algorithm to establish the similar day analysis-firefly algorithm. By combining the two sub-models with information entropy, a hybrid prediction model for photovoltaic power generation based on information entropy is proposed. The experimental results show that in various weather conditions, the values of mean absolute percentage error, root mean square error and standard deviation of error are 2.8425%, 2.5675 and 2.2642, respectively. Therefore, the proposed hybrid model has superior prediction performance. 1 INTRODUCTION Nowadays, due to the challenges of climate change and energy crisis, more and more attention is paid to using renewable energy in the world. Solar energy has the unique advantages of rich resources, no air pollution and wide distribution. According to Renewables 2020 Global Status Report, by the end of 2019, the solar photovoltaic global capacity has reached 627 GW, an increase of 22.46% compared with 2018. Nowadays, the output power of large-scale photovoltaic plants has exceeded 500 MW, such as Gujarat Solar Park in India and Topaz Solar Farm in the

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“…Although combining forecasts is already a well-known method for improving forecast accuracy, with a wide range of approaches such as simple average and Bayesian methods, this approach is still underdeveloped [24]. The disadvantage of several combined forecast models is the missing dynamic adjustment of the combination weights, which can miss the real-time changes in RE generation [25]. A corresponding dynamic combination forecast yielded improved results in [25,26].…”

mentioning

confidence: 99%

“…The disadvantage of several combined forecast models is the missing dynamic adjustment of the combination weights, which can miss the real-time changes in RE generation [25]. A corresponding dynamic combination forecast yielded improved results in [25,26]. However, these were forecast models at the distribution level.…”

mentioning

confidence: 99%

Substation Related Forecasts of Electrical Energy Storage Systems: Transmission System Operator Requirements

et al. 2020

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The growth in volatile renewable energy (RE) generation is accompanied by an increasing network load and an increasing demand for storage units. Household storage systems and micro power plants, in particular, represent an uncertainty factor for distribution networks, as well as transmission networks. Due to missing data exchanges, transmission system operators cannot take into account the impact of household storage systems in their network load and generation forecasts. Thus, neglecting the increasing number of household storage systems leads to increasing forecast inaccuracies. To consider the impact of the storage systems on forecasting, this paper presents a new approach to calculate a substation-specific storage forecast, which includes both substation-specific RE generation and load forecasts. For the storage forecast, storage systems and micro power plants are assigned to substations. Based on their aggregated behavior, the impact on the forecasted RE generation and load is determined. The load and generation are forecasted by combining several optimization approaches to minimize the forecasting errors. The concept is validated using data from the German transmission system operator, 50 Hertz Transmission GmbH. This investigation demonstrates the significance of using a battery storage forecast with an integrated load and generation forecast.

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Short-Term Photovoltaic Power Generation Combination Forecasting Method Based on Similar Day and Cross Entropy Theory

Cited by 22 publications

References 13 publications

An Efficient Algorithm for Power Prediction in PV Generation System

An Efficient Algorithm for Power Prediction in PV Generation System

A hybrid prediction model for photovoltaic power generation based on information entropy

Substation Related Forecasts of Electrical Energy Storage Systems: Transmission System Operator Requirements

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