Photovoltaic output prediction of regional energy Internet based on LSTM algorithm

Weng, Geping; Pei, Chuanxun; Ren, Jiaorong; Wang, Hongzhi; Cui, Qinyue; Qing, Hua; Liu, Yuan; Guan, Xinyu

doi:10.1088/1742-6596/1732/1/012083

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…Weng et al believe that with the development of modern industry and the continuous growth of the urban population, more and more sewage discharge has not only caused great damage to the whole ecological environment but also caused a serious waste of resources, thus affecting people's normal life and work. In addition, due to the limited freshwater resources in China, many places require that the discharge of sewage treatment system must meet the reuse standard to realize the recycling of sewage treatment [8]. Li et al believe that in order to ensure the sustainable development of China's economy, we must explore more advanced sewage treatment processes, technologies, and countermeasures to achieve effective treatment and recycling of sewage.…”

Section: Literature Reviewmentioning

confidence: 99%

“…e other parameter settings are as follows: FWA and CFWA: explosion radius adjustment constant R � 240, adjustment constant of explosion spark number M � 200, upper limit of explosive sparks am � 20, lower limit of explosive sparks bm � 1, and Gaussian variation spark number G � 50. e chaotic algorithm adopts the improved sinusoidal chaotic map SM proposed in this paper. See equation (8) for details. PPSO: c1 � c2 � 2.0, ω max � 0.60, and ω min � 0.06.…”

Section: Performance Testmentioning

confidence: 99%

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Prediction of Dissolved Oxygen Concentration in Sewage Treatment Process Based on Data Recognition Algorithm

Liu

2022

International Journal of Analytical Chemistry

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In order to realize the real-time and accurate prediction of dissolved oxygen concentration in the sewage treatment process, a prediction model of dissolved oxygen concentration in the sewage treatment process based on a data identification algorithm was proposed. Combined with the data characteristics of the sewage treatment process, a new sample similarity measure is defined to extract more representative modeling data. In the improved algorithm, in order to improve the quality of the initial members of the basic fireworks algorithm, the chaos algorithm is integrated. The search mechanism of the basic fireworks algorithm is improved, and the optimization process is divided into two stages based on the set criteria, and two groups are used simultaneously. The results show that compared with the basic FWA algorithm, the CFWA algorithm makes better use of the chaotic search mechanism. On the one hand, it avoids the excessive random or blind selection of the initial weight threshold of the neural network in the initial stage; on the other hand, in the optimization process of the weight threshold, two types of search mechanisms, FWA and COA, are used to give full play to their respective strengths and to continuously conduct information exchange and mutual cooperation between groups and individuals. The number of times is better than the basic FWA algorithm, and the training error and generalization error of the CFWA model in the simulation results of the soft sensor model are also better than those of the FWA model, which fully verifies the effectiveness of the CFWA algorithm. It is proved that the data recognition algorithm can effectively predict sewage treatment. It is proved that the data recognition algorithm can effectively predict the dissolved oxygen concentration in wastewater treatment process. It provides a new measurement method for some key process variables that cannot be measured or are difficult to measure in complex chemical processes.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Performance Testmentioning

confidence: 99%

Prediction of Dissolved Oxygen Concentration in Sewage Treatment Process Based on Data Recognition Algorithm

Liu

2022

International Journal of Analytical Chemistry

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“…The digital twin system not only forms the mirror image of the physical entity object in the real physical domain, but also realizes two-way data transmission with the physical entity object [7] . On the one hand, it accepts the real-time information of the real physical domain, on the other hand, it drives the real physical domain in turn to realize the control of the physical entity object.…”

Section: Multi-dimensional Situation Prediction Methods Of Digital Tw...mentioning

confidence: 99%

Multi-dimensional situation prediction of digital twin active power grid based on LSTM algorithm

Wang,

TAN,

et al. 2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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In order to understand the multidimensional situation prediction of digital twin active power grid, a research on multidimensional situation prediction of digital twin active power grid based on LSTM algorithm is proposed. In this paper, firstly, a multi-dimensional situation prediction algorithm of power grid key indicators based on LSTM is established to realize the change prediction of key indicators attributes of digital twin active power grid. Secondly, the data of several key indicators such as load characteristics are collected, and a multi-dimensional system prediction model is established, which can control the state of active power grid; The LSTM prediction algorithm is proposed to fit the characteristics of multi-dimensional data, and the next stage of multi-dimensional data prediction is mapped to the power digital twin, so as to realize the synchronous implementation and intelligent regulation of smart energy system operation planning. Finally, a simulation test model is established, and an example shows that the multi-dimensional situation prediction method of digital twin power grid based on deep learning can better predict and distinguish the power grid situation, and provide decision support for accurate planning of energy system in the future.

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Application of machine learning methods in photovoltaic output power prediction: A review

Zhang

2022

Journal of Renewable and Sustainable Energy

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As the proportion of photovoltaic (PV) power generation rapidly increases, accurate PV output power prediction becomes more crucial to energy efficiency and renewable energy production. There are numerous approaches for PV output power prediction. Many researchers have previously summarized PV output power prediction from different angles. However, there are relatively few studies that use machine learning methods as a means to conduct a separate review of PV output power prediction. This review classifies machine learning methods from different perspectives and provides a systematic and critical review of machine learning methods for recent PV output power applications in terms of the temporal and spatial scales of prediction and finds that the artificial neural network and support vector machine are used much more frequently than other methods. In addition, this study examines the differences between the output power prediction of individual PV plants and regional PV stations and the benefits of regional PV plant prediction, while this paper presents some performance evaluation matrices commonly used for PV output power prediction. In addition, to further improve the accuracy of machine learning methods for PV output power prediction, some researchers suggest preprocessing the input data of the prediction models or considering hybrid machine learning methods. Furthermore, the potential advantages of machine model optimization for prediction performance improvement are discussed and explored in detail.

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Photovoltaic output prediction of regional energy Internet based on LSTM algorithm

Cited by 4 publications

References 8 publications

Prediction of Dissolved Oxygen Concentration in Sewage Treatment Process Based on Data Recognition Algorithm

Prediction of Dissolved Oxygen Concentration in Sewage Treatment Process Based on Data Recognition Algorithm

Multi-dimensional situation prediction of digital twin active power grid based on LSTM algorithm

Application of machine learning methods in photovoltaic output power prediction: A review

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