Short-Term Prediction Method of Solar Photovoltaic Power Generation Based on Machine Learning in Smart Grid

Liu, Yuanyuan

doi:10.1155/2022/8478790

Cited by 14 publications

(7 citation statements)

References 20 publications

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“…The advantages of using GRU in energy storage capacity planning and dispatch optimization include its ability to handle sequential data and its superior performance compared to traditional machine learning models. GRU can effectively capture the temporal dependencies between energy data, and make accurate predictions of future energy demand and supply (Liu, 2022). Moreover, GRU can optimize the energy storage capacity and dispatch in real-time, which is crucial for the efficient and reliable operation of intelligent power grids.…”

Section: Gru Modelmentioning

confidence: 99%

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Smart grid energy storage capacity planning and scheduling optimization through PSO-GRU and multihead-attention

Xiao,

Zhang

2023

Front. Energy Res.

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The energy problem in today’s society is becoming increasingly prominent, and the smart grid has become one of the important ways to solve the energy problem. Smart grid energy storage capacity planning and scheduling optimization is an important issue in the smart grid, which can make the grid more efficient, reliable, and sustainable to meet energy demand better and protect the environment. The core of smart grid energy storage capacity planning and scheduling optimization is maximizing the use of energy storage devices to balance the difference between power supply and demand to ensure the grid operation’s stability. Traditional planning methods are usually based on experience and rules, have low precision, and cannot adapt to the dynamic changes in the long-term development of the power grid. Therefore, this paper proposes a method that combines PSO-GRU (particle swarm Optimization (PSO)-gated recurrent unit (GRU)) and Multihead-Attention to realize smart grid energy storage capacity planning. And scheduling optimization. First, PSO-GRU models and predicts power grid data by searching for the optimal GRU model parameters; second, Multihead-Attention improves the model’s performance through the self-attention mechanism. Finally, we use the method to determine the optimal energy storage capacity and dispatching scheme for the efficient operation of smart grids. Our experiments use real power grid datasets and compare them with other common methods. Experimental results show that our proposed method has higher accuracy and stability than other methods and can better adapt to the dynamic changes of the power grid. This indicates that our method has good feasibility and applicability in practical applications and is significant for realizing the efficient operation of smart grids and energy saving and emission reduction.

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Section: Gru Modelmentioning

confidence: 99%

“…In our proposed method, we use the GRU model to model and predict grid data for grid energy storage capacity planning and dispatch optimization (Liu, 2022). The GRU is a recurrent neural network that can be used to model sequence data.…”

Section: Gru Modelmentioning

confidence: 99%

Smart grid energy storage capacity planning and scheduling optimization through PSO-GRU and multihead-attention

Xiao,

Zhang

2023

Front. Energy Res.

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“…The results show that the accuracy and efficiency of the model are relatively high. Liu et al [15] proposed a short-term PV power generation prediction model based on whale optimization algorithm to optimize support vector machine parameters. The prediction results of the optimized algorithm model are compared with those of SVM, PSO-SVM and ARIMA.…”

Section: Introductionmentioning

confidence: 99%

Power Material Demand Forecasting Based on LSTM and Random Forecast

Song,

Jiao

2024

Advances in Transdisciplinary Engineering

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This paper takes an electric power company as a case study. Through actual investigation and analysis of the data provided by the company, we found that the material demand forecast of the company is not accurate enough, so that the company cannot effectively arrange the material purchase, resulting in a large backlog of inventory. To improve the reliability and accuracy of the power material demand forecast of the company, this paper proposes a combined forecasting model which combines Long Short-Term Memory Network (LSTM) model and Random Forest (RF) algorithm to predict power material demand. First, the LSTM model is used for preliminary prediction, and then RF algorithm is used to further predict and optimize the residual of LSTM prediction, so as to improve the overall prediction effect. Finally, the combined prediction model proposed in this paper is compared with the single LSTM model and RF algorithm, and it is verified that the combined prediction model has higher prediction accuracy. The research on the demand forecast provides a valuable reference for the company to make material purchase plan and solve the problem of high inventory, and affords a certain reference for the material demand forecast of many similar industries.

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“…For example, Lucas et al [10] found that extreme solar irradiance events can have adverse effects on the fuses and inverters of PV generators. In addition, some other studies focus on the research of various artificial intelligence algorithms [11][12][13] such as the adaptive k-means algorithm [14], Gated Recurrent Unit networks [15], Long Short-Term Memory neural networks [16], and Convolutional Neural Network-based Informer models [17] for PV forecasting [18][19][20]. Generally, despite extensive studies examining the impact of climatic factors on PV performance and the application of artificial intelligence (AI) technologies for PV power forecasting, research on predicting the performance of PV systems under extreme climate conditions, particularly during high-temperature heatwaves and non-heatwave conditions, remains limited.…”

Section: Introductionmentioning

confidence: 99%

Response of Sustainable Solar Photovoltaic Power Output to Summer Heatwave Events in Northern China

Huang,

Duan,

Zhang

et al. 2024

Sustainability

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Understanding the resilience of photovoltaic (PV) systems to extreme weather, such as heatwaves, is crucial for advancing sustainable energy solutions. Although previous studies have often focused on forecasting PV power output or assessing the impact of geographical variations, the dynamic response of PV power outputs to extreme climate events still remains highly uncertain. Utilizing the PV power data and meteorological parameters recorded at 15 min intervals from 1 July 2018 to 13 June 2019 in Hebei Province, this study investigates the spatiotemporal characteristics of the PV power output and its response to heatwaves. Solar radiation and air temperature are pivotal in enhancing PV power output by approximately 30% during heatwave episodes, highlighting the significant contribution of PV systems to energy supplies under extreme climate conditions. Furthermore, this study systematically evaluates the performance of Random Forest (RF), Decision Tree Regression (DTR), Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), Deep Belief Network (DBN), and Multilayer Perceptron (MLP) models under both summer heatwave and non-heatwave conditions. The findings indicate that the RF and LightGBM models exhibit higher predictive accuracy and relative stability under heatwave conditions, with an R2 exceeding 0.98, with both an RMSE and MAE below 0.47 MW and 0.24 MW, respectively. This work not only reveals the potential of machine learning to enhance our understanding of climate–energy interplay but also contributes valuable insights for the formulation of adaptive strategies, which are critical for advancing sustainable energy solutions in the face of climate change.

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Short-Term Prediction Method of Solar Photovoltaic Power Generation Based on Machine Learning in Smart Grid

Cited by 14 publications

References 20 publications

Smart grid energy storage capacity planning and scheduling optimization through PSO-GRU and multihead-attention

Smart grid energy storage capacity planning and scheduling optimization through PSO-GRU and multihead-attention

Power Material Demand Forecasting Based on LSTM and Random Forecast

Response of Sustainable Solar Photovoltaic Power Output to Summer Heatwave Events in Northern China

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