Spatial-Temporal Genetic-Based Attention Networks for Short-Term Photovoltaic Power Forecasting

Renewable energy resources can be deployed locally and efficiently using the concept of microgrids. Due to the natural uncertainty of the output power of renewable energy resources, the planning for a proper operation of microgrids can be a challenging task. In addition, the information about the loads and the power consumption of them can create benefits to increase the efficiency of the microgrids. However, electrical loads can have uncertainty due to reasons such as unpredictable behavior of the consumers. To exploit a microgrid, energy management is required at the upper level of operation and control in order to reduce the costs. One of the most important tasks of the energy management system is to satisfy the loads and, in other words, develop a plan to maintain equilibrium between the power generation and power consumption. To obtain information about the output power of renewable energy resources and power consumption, deep learning can be implemented as a powerful tool, which is able to predict the desired values. In addition, weather conditions can affect the output power of renewable energy-based resources and the behavior of the consumers and, as a result, the power consumption. So, deep learning can be deployed for the anticipation of the weather conditions. This paper will study the recent works related to deep learning, which has been implemented for the prediction of the output power of renewable energy resources (i.e., PVs and wind turbines), electrical loads, and weather conditions (i.e., solar irradiance and wind speed). In addition, for possible future directions some strategies are suggested, the most important of which is the implementation of quantum computing in cyber–physical microgrids.

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“…Additionally, for more study about the prediction for PVs, refs. [78][79][80][81][82][83][84][85][86][87] can be studied.…”

Section: Pv Output Predictionmentioning

confidence: 99%

Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

et al. 2023

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“…They are capable of effectively solving various hyperparameter-optimization combination problems [31]. Examples of such algorithms include genetic algorithm (GA) [40][41][42], particleswarm optimization (PSO) [43,44], firefly algorithm (FA) [45], ant-colony algorithm (ACO) [21], bat algorithm (BA) [46,47], and others.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Power-Generation Prediction of High Humidity Island Photovoltaic Power Station Based on a Deep Hybrid Model

Wang,

Jia,

et al. 2024

Sustainability

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Precise prediction of the power generation of photovoltaic (PV) stations on the island contributes to efficiently utilizing and developing abundant solar energy resources along the coast. In this work, a hybrid short-term prediction model (ICMIC-POA-CNN-BIGRU) was proposed to study the output of a fishing–solar complementary PV station with high humidity on the island. ICMIC chaotic mapping was used to optimize the initial position of the pelican optimization algorithm (POA) population, enhancing the global search ability. Then, ICMIC-POA performed hyperparameter debugging and L2-regularization coefficient optimization on CNN-BIGRU (convolutional neural network and bidirectional gated recurrent unit). The L2-regularization technique optimized the loss curve and over-fitting problem in the CNN-BIGRU training process. To compare the prediction effect with the other five models, three typical days (sunny, cloudy, and rainy) were selected to establish the model, and six evaluation indexes were used to evaluate the prediction performance. The results show that the model proposed in this work shows stronger robustness and generalization ability. K-fold cross-validation verified the prediction effects of three models established by different datasets for three consecutive days and five consecutive days. Compared with the CNN-BIGRU model, the RMSE values of the newly proposed model were reduced by 64.08%, 46.14%, 57.59%, 60.61%, and 34.04%, respectively, in sunny, cloudy, rainy, continuous prediction 3 days, and 5 days. The average value of the determination coefficient R2 of the 20 experiments was 0.98372 on sunny days, 0.97589 on cloudy days, and 0.98735 on rainy days.

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“…Others have added a fourth category based on the requirements of the decision-making process for smart grids or microgrids, aptly named very short-term or ultra-short-term (less than 1 hour) forecast horizon. However, so far there is no universally agreed classification criterion [4].…”

Section: Introductionmentioning

confidence: 99%

Photovoltaic generation prediction using the deep learning long short term memory model

Casanova

Viltres

2022

ITEGAM JETIA

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Solar photovoltaic energy is a renewable, clean and safe energy source that is currently used worldwide. However, it presents a dynamic and intermittent behavior, caused by the variation of climatic conditions. Due to this, it has been necessary to develop different methods for the prediction of the energy generated in photovoltaic systems. The present work is focused on analyzing the prediction of the power generated in a photovoltaic plant connected to the grid, by means of the Long Short-Term Memory (LSTM) deep learning model. In order to carry out the study, a database obtained from the photovoltaic plant of the Central University "Marta Abreu" of Las Villas (UCLV) with a nominal installed power of 1.1 MW is used. Initially, the correlation between the different variables with respect to the photovoltaic power generated is analyzed, then the LSTM model is implemented to make the prediction. The results obtained show that the predictions made for different time horizons and for days with different behavior are adequate, which demonstrates the effectiveness of this prediction method.

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Spatial-Temporal Genetic-Based Attention Networks for Short-Term Photovoltaic Power Forecasting

Cited by 12 publications

References 34 publications

Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

Short-Term Power-Generation Prediction of High Humidity Island Photovoltaic Power Station Based on a Deep Hybrid Model

Photovoltaic generation prediction using the deep learning long short term memory model

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