Hybrid model based on K-means++ algorithm, optimal similar day approach, and long short-term memory neural network for short-term photovoltaic power prediction

Bai, Ruxue; Shi, Yuetao; Yue, Meng; Du, Xiaonan

doi:10.1016/j.gloei.2023.04.006

Cited by 16 publications

(3 citation statements)

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“…A short-term forecasting model for predicting inside temperature in residential buildings using a sequence generative adversarial network can utilize historical data such as outdoor and past internal temperatures to generate an artificial dataset of previous temperatures [11]. Then, an autoregressive deep neural network can be trained to provide a "synthetic" forecast primarily based on this information, allowing it to learn the patterns that may potentially arise in temperature behaviors in a specific location [12]. Subsequently, the model ought to be scrutinized by evaluating the predictions generated by the version and contrasting them with the observed data.…”

Section: Methodsmentioning

confidence: 99%

Exploring Long Short Term Memory Algorithms for Low Energy Data Aggregation

2024

JMC

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Long short-term memory methods are employed for data consolidation in intricate low-energy devices. It has enabled accurate and efficient aggregation of statistics in limited electricity settings, facilitating the review and retrieval of data while minimizing electricity wastage. The LSTM rules analyze, organize, and consolidate vast datasets inside weakly connected structures. It has employed a recurrent neural network to handle data processing, particularly nonlinear interactions. The machine's capabilities are subsequently examined and stored utilizing memory blocks. Memory blocks retain extended temporal connections within the data, facilitating adaptive and precise information aggregation. These blocks facilitate the system's ability to shop and utilize relevant capabilities for quick retrieval. The proposed algorithm offers realistic tuning capabilities such as learning rate scheduling and total regularization based on dropout like green information aggregation. These enable systems to reduce over fitting while permitting precise adjustment of the settings. It allows for optimizing the algorithm to provide highly dependable performance within weak structures, enhancing data aggregation techniques' energy efficiency. Standard algorithms provide an efficient, accurate solution for aggregating information in low-power systems. It facilitates evaluating, retrieving, and aggregating accurate and reliable information using memory blocks, adaptive tuning, and efficient learning rate scheduling.

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Section: Methodsmentioning

confidence: 99%

Exploring Long Short Term Memory Algorithms for Low Energy Data Aggregation

2024

JMC

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“…The method of PV clustering is also reflected in many studies, and an equivalent computational model can be obtained by analyzing the clustering of high-density distributed PVs connected to the distribution grid [31]. Studies have also presented the clustering method in detail and have demonstrated how it works in simulation models [32][33][34][35][36]. According to different control strategies, different system models have been developed for full utilization of energy in different scenarios [37].…”

Section: Introductionmentioning

confidence: 99%

Optimal Dispatch Strategy for a Distribution Network Containing High-Density Photovoltaic Power Generation and Energy Storage under Multiple Scenarios

Hou,

Chen,

Wang

et al. 2023

Inventions

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To better consume high-density photovoltaics, in this article, the application of energy storage devices in the distribution network not only realizes the peak shaving and valley filling of the electricity load but also relieves the pressure on the grid voltage generated by the distributed photovoltaic access. At the same time, photovoltaic power generation and energy storage cooperate and have an impact on the tidal distribution of the distribution network. Since photovoltaic output has uncertainty, the maximum photovoltaic output in each scenario is determined by the clustering algorithm, while the storage scheduling strategy is reasonably selected so the distribution network operates efficiently and stably. The tidal optimization of the distribution network is carried out with the objectives of minimizing network losses and voltage deviations, two objectives that are assigned comprehensive weights, and the optimization model is constructed by using a particle swarm algorithm to derive the optimal dispatching strategy of the distribution network with the cooperation of photovoltaic and energy storage. Finally, a model with 30 buses is simulated and the system is optimally dispatched under multiple scenarios to demonstrate the necessity of conducting coordinated optimal dispatch of photovoltaics and energy storage.

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“…LSTM consists of three gates: the input gate, the forget gate, and the output gate. The structure diagram of LSTM [33][34] is shown in Figure 4. The specific definitions of each part in the LSTM cell are given as follows.…”

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confidence: 99%

The Prediction of Flow in Railway Station Based on RRC-STGCN

Wang,

Bai,

Meng

et al. 2023

IEEE Access

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Prediction of the passenger flow is of great importance to the management and safety in railway stations. Accurate prediction of passenger flow facilitates effective allocation of staff tasks, enhances the efficient utilization of waiting areas, ensures passenger safety, and promotes a smooth travel experience for passengers. However, accurately establishing the spatial-temporal relationship of passenger flow within the station and predicting the passenger flow in each region and time period is challenging due to the varying waiting habits and train preferences of each individual passenger. In this paper, we propose a Residual-RNN-Channel Spatial-Temporal Graph Convolutional Network (RRC-STGCN), which utilizes the channel attention mechanism and residual structure. The model divides the data with a time dimension into multiple periods, capturing spatial-temporal correlations through the channel attention mechanism, and extracting spatial-temporal dependencies from the feature maps using the spatial-temporal convolution module. The model uses a residual structure to fuse features in order to enhance the accuracy of prediction results. In addition, we conduct a comprehensive experimental evaluation using a real dataset of railway station passenger, demonstrating that the RRC-STGCN model outperforms five well-known baselines. Moreover, we provide visualizations of the prediction results, effectively showcasing the dynamic changes in passenger flow in each waiting area.

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Hybrid model based on K-means++ algorithm, optimal similar day approach, and long short-term memory neural network for short-term photovoltaic power prediction

Cited by 16 publications

References 0 publications

Exploring Long Short Term Memory Algorithms for Low Energy Data Aggregation

Exploring Long Short Term Memory Algorithms for Low Energy Data Aggregation

Optimal Dispatch Strategy for a Distribution Network Containing High-Density Photovoltaic Power Generation and Energy Storage under Multiple Scenarios

The Prediction of Flow in Railway Station Based on RRC-STGCN

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