Based on the historical time series data and irradiation data of the photovoltaic power plant, a model GRU for short-term power prediction of the photovoltaic power plant is proposed, which uses a gated recurrent unity neural network. GRU can quickly and efficiently extract the effective features of the data, input the data of ...
PV power generation forecasting is long-term by considering climatic data such as solar irradiance, temperature and humidity. Moreover, we implemented these deep learning methods on two datasets, the first one is made of electrical consumption data collected from smart meters installed at consumers in Douala.
This solar power plant serves as a central component of the infrastructure under investigation (see Fig. 8 ). Adjacent to the solar power plant is a greenhouse, a controlled environment designed to cultivate and study plant specimens.
Table 8. Comparison with the literature on PV power generation forecasting. that the proposed hybrid model is better than those in the literature with minimum error and highest regression. 4. Conclusion This study aims to present deep learning algorithms for electrical demand prediction and solar PV power generation forecasting.
In our knowledge, it is the first paper which can both forecast the electrical load and PV power generation using large amount of historical data for long term predictions. Moreover, the novel multi-objective deep learning model proposed in the paper can help power distributors for vulgarization and integration of renewable energy in the future.
Its general application is discussed. Solar photovoltaic (PV) power generation, as a clean and renewable energy source, has significant environmental and economic benefits.
Hour-ahead predictions consider factors such as cloud cover, atmospheric conditions, and the sun's angle to estimate the sunlight reaching solar panels in the upcoming hour. The proposed model aims to predict solar power generation with high precision, facilitating proactive energy management and optimization.
Based on the historical time series data and irradiation data of the photovoltaic power plant, a model GRU for short-term power prediction of the photovoltaic power plant is proposed, which uses a gated recurrent unity neural network. GRU can quickly and efficiently extract the effective features of the data, input the data of ...
In a typical solar power generation system, the sunlight strikes the solar panels, generating DC electricity in the photovoltaic (PV) cells. The DC voltage travels through cables to the inverter and the inverter converts the DC electricity into AC electricity. The AC voltage can then be used to power home or business appliances. The following are the details of the basic …
The accurate forecasting of solar energy generation, contingent on weather conditions, holds paramount importance for proactive energy management. The proposed SSA-CNN-LSTM model is intricately designed to predict solar power generation with high precision through historical data preprocessing techniques.
Because solar power generation is intrinsically highly dependent on weather fluctuations, predicting power generation using weather information has several economic benefits, including reliable ...
Focusing on a 24-hour-ahead prediction problem, the authors first design two deep neural networks for prediction: a deep feedforward network that uses the weather forecast data and a recurrent neural network that uses recent weather observations. Finally, a hybrid network, named PVHybNet, combines the both networks to enhance their ...
The application of deep learning in solar power prediction greatly improves the accuracy and reliability of the prediction by constructing complex neural network architectures, and the powerful nonlinear modeling capability of deep learning models makes them better able to handle the complexity and uncertainty of the solar power generation process, such as the …
Photovoltaic power generation is a technology that uses solar panels to convert light energy directly into electricity but is not equipped with an energy storage system, generates unstable power ...
This paper addresses the challenge of accurately forecasting solar power generation (SPG) across multiple sites using a single common model. The proposed deep learning-based model is designed to predict SPG …
The study aims to predict solar energy generation to ensure successful operation of solar power plants and tackle issues such as increasing energy demand, global warming, and greenhouse …
This study aims to present deep learning algorithms for electrical demand prediction and solar PV power generation forecasting. Therefore, we proposed a novel multi-objective hybrid model named FFNN …
Short-Term Solar PV Generation Forecast Using Neural Networks and Deep Learning Models Shivashankar Sukumar, Naran M. Pindoriya, and Sri Niwas Singh 7.1 Introduction Renewable energy sources are the critical component of sustainable development as they reduce the dependency on fossil fuels for power generation, reduce greenhouse gas emission, and …
Solar power prediction is a critical aspect of optimizing renewable energy integration and ensuring efficient grid management. The chapter explore the application of artificial intelligence (AI) techniques for accurate solar power forecasting. The AI models considered include Artificial Neural Networks (ANN), Support Vector Machines (SVM), …
Achieving accurate predictions for power generation is important to provide high-quality electric energy for end-users. Therefore, in this paper, we introduce a deep learning-based dual-stream convolutional neural network (CNN) and long short-term nemory (LSTM) network followed by a self-attention mechanism network (DSCLANet).
Achieving accurate predictions for power generation is important to provide high-quality electric energy for end-users. Therefore, in this paper, we introduce a deep learning-based dual-stream convolutional neural network …
The massive deployment of photovoltaic solar energy generation systems represents a concrete and promising response to the environmental and energy challenges of our society [].Moreover, the integration of renewable energy sources in the traditional network leads to the concept of smart grid [].According to author [], the smart grid is the new evolution of the …
Focusing on a 24-hour-ahead prediction problem, the authors first design two deep neural networks for prediction: a deep feedforward network that uses the weather …
The study aims to predict solar energy generation to ensure successful operation of solar power plants and tackle issues such as increasing energy demand, global warming, and greenhouse gas emissions. The study uses multiple linear regression and feature selection to calculate energy generation, long short‐term memory to predict energy ...
The utilization of Geographic Information Systems (GIS) and Deep Neural Networks (DNN) in predicting solar power plant production plays a pivotal role in promoting sustainable energy development by identifying …
This article focuses on the study of PV power generation forecasting based on SCADA data. A method for the PV power generation prediction is developed. It includes data collection and processing, radar chart generation using the selected parameter signals, selection and training of the deep learning network models, and model application for PV ...
However, the highly variable nature of solar radiation poses unique challenges for accurately predicting solar photovoltaic (PV) power generation. Factors such as cloud cover, atmospheric conditions, and seasonal variations significantly impact the amount of solar energy available for conversion into electricity. Therefore, it is essential to precisely estimate the …
Based on the historical time series data and irradiation data of the photovoltaic power plant, a model GRU for short-term power prediction of the photovoltaic power plant is …
Solar photovoltaic (PV) systems have drawn significant attention over the last decade. One of the most critical obstacles that must be overcome is distributed energy generation. This paper presents a comprehensive quantitative bibliometric study to identify the new trends and call attention to the evolution within the research landscape concerning the …
In this paper, a novel DBN modeling approach for solar power generation forecasting in solar plants was proposed by fusing multi-source information, including sensor data, operational indicators, meteorological data, lagged AC power information, and error nodes. The main conclusions are summarized as follows.
In this paper, a novel DBN modeling approach for solar power generation forecasting in solar plants was proposed by fusing multi-source information, including sensor …
The utilization of Geographic Information Systems (GIS) and Deep Neural Networks (DNN) in predicting solar power plant production plays a pivotal role in promoting sustainable energy development by identifying optimal locations for solar energy generation. This innovative technology combines spatial data analysis and machine learning ...
This study aims to present deep learning algorithms for electrical demand prediction and solar PV power generation forecasting. Therefore, we proposed a novel multi-objective hybrid model named FFNN-LSTM-MOPSO which is efficient in data training and optimization of input parameters. These deep learning models were implemented on socio …
As global carbon reduction initiatives progress and the new energy sector rapidly develops, photovoltaic (PV) power generation is playing an increasingly significant role in renewable energy. Accurate PV output forecasting, influenced by meteorological factors, is essential for efficient energy management. This paper presents an optimal hybrid forecasting …
This article focuses on the study of PV power generation forecasting based on SCADA data. A method for the PV power generation prediction is developed. It includes data …
This paper addresses the challenge of accurately forecasting solar power generation (SPG) across multiple sites using a single common model. The proposed deep learning-based model is designed to predict SPG for various locations by leveraging a comprehensive dataset from multiple sites in the Republic of Korea. By incorporating common ...
The accurate forecasting of solar energy generation, contingent on weather conditions, holds paramount importance for proactive energy management. The proposed …
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