Power output forecasting of solar photovoltaic plant using LSTM

Dhaked, Dheeraj Kumar; Dadhich, Sharad; Birla, Dinesh

doi:10.1016/j.geits.2023.100113

Cited by 33 publications

(11 citation statements)

References 27 publications

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“…Comparison between time series methods and artificial intelligence-based methods for power output prediction in a large grid-connected PV www.astesj.com www.astesj.com plant in China indicated the efficiency of neural network models over statistical models for PV power output prediction, particularly for short-term forecasts [5]. In a solar power prediction study in India, the efficiency of Long Short-Term Memory (LSTM) and Backpropagation Neural Network (BPNN) models was compared, confirming the effectiveness of the LSTM model [3]. LSTM and Multi-layer Perception (MLP) techniques were employed to forecast short-term solar PV power.…”

Section: Introductionmentioning

confidence: 91%

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Solar Photovoltaic Power Output Forecasting using Deep Learning Models: A Case Study of Zagtouli PV Power Plant

Palm,

Ezéckiel Houénafa,

Boubakar

et al. 2024

Adv. sci. technol. eng. syst. j.

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Forecasting solar PV power output holds significant importance in the realm of energy management, particularly due to the intermittent nature of solar irradiation. Currently, most forecasting studies employ statistical methods. However, deep learning models have the potential for better forecasting. This study utilises Long Short-Term Memory (LSTM), Gate Recurrent Unit (GRU) and hybrid LSTM-GRU deep learning techniques to analyse, train, validate, and test data from the Zagtouli Solar Photovoltaic (PV) plant located in Ouagadougou (longitude:12.30702 o and latitude:1.63548 o ), Burkina Faso. The study involved three evaluation metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R 2 ). The RMSE evaluation criteria gave 10.799(LSTM), 11.695(GRU) and 10.629(LSTM-GRU) giving the LSTM-GRU model as the best for RMSE evaluation. The MAE evaluation provided 2.09, 2.1 and 2.0 for the LSTM,

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Section: Introductionmentioning

confidence: 91%

“…The application of deep learning techniques has gained considerable attention in this context due to its capacity to model complex relationships within large datasets, offering a promising tool to enhance the precision of solar PV power output forecasting [3].…”

Section: Introductionmentioning

confidence: 99%

Solar Photovoltaic Power Output Forecasting using Deep Learning Models: A Case Study of Zagtouli PV Power Plant

Palm,

Ezéckiel Houénafa,

Boubakar

et al. 2024

Adv. sci. technol. eng. syst. j.

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“…The input layer consists of two layers: a sigmoid layer and a hyperbolic tangent (tanh) layer. The sigmoid layer decides which input values should be updated through the function i t shown in Equation (2). The hyperbolic tangent layer creates a vector of new values that can be added to the cell state line Ct , as shown in Equation (3) [14].…”

Section: How An Lstm Cell Workmentioning

confidence: 99%

“…The presence of artificial intelligence is continually growing, and as a result, there are currently numerous research efforts related to solar energy production using this technology. The authors [2], in their study, forecast energy production at photovoltaic solar plants using long short-term memory (LSTM) models and a back-propagation neural network (BPNN). The forecast was made for a 15-min horizon based on information from the previous hour.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Forecast of Photovoltaic Solar Energy Production Using LSTM

Campos,

Sousa,

Barbosa

2024

Energies

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In recent times, renewable energy sources have gained considerable vitality due to their inexhaustible resources and the detrimental effects of fossil fuels, such as the impact of greenhouse gases on the planet. This article aims to be a supportive tool for the development of research in the field of artificial intelligence (AI), as it presents a solution for predicting photovoltaic energy production. The basis of the AI models is provided from two data sets, one for generated electrical power and another for meteorological data, related to the year 2017, which are freely available on the Energias de Portugal (EDP) Open Project website. The implemented AI models rely on long short-term memory (LSTM) neural networks, providing a forecast value for electrical energy with a 60-min horizon based on meteorological variables. The performance of the models is evaluated using the performance indicators MAE, RMSE, and R2, for which favorable results were obtained, with particular emphasis on forecasts for the spring and summer seasons.

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“…Zhang et al [25] proposed a joint trans-fer learning method for mechanical fault diagnosis. Dhaked et al [26] proposed using LSTM to predict the power generation of solar power plants. Zhao and colleagues [27] proposed a convolutional deep belief network to learn the representative features of bearing faults for classification.…”

Section: Introductionmentioning

confidence: 99%

Lightweight RepVGG-Based Cross-Modality Data Prediction Method for Solid Rocket Motors

Yang,

Zheng,

Wang

et al. 2023

Sensors

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Solid rocket motors (SRMs) have been popularly used in the current aerospace industry. Performance indicators, such as pressure and thrust, are of great importance for rocket monitoring and design. However, the measurement of such signals requires high economic and time costs. In many practical situations, the thrust measurement error is large and requires manual correction. In order to address this challenging problem, a lightweight RepVGG-based cross-modality data prediction method is proposed for SRMs. An end-to-end data prediction framework is established by transforming data across different modalities. A novel RepVGG deep neural network architecture is built, which is able to automatically learn features from raw data and predict new time-series data of different modalities. The effectiveness of the proposed method is extensively validated with the field SRM data. The accurate prediction of the thrust data can be achieved by exploring the pressure data. After calculation, the percentage error between the predicted data and the actual data is less than 5%. The proposed method offers a promising tool for cross-modality data prediction in real aerospace industries for SRMs.

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Power output forecasting of solar photovoltaic plant using LSTM

Cited by 33 publications

References 27 publications

Solar Photovoltaic Power Output Forecasting using Deep Learning Models: A Case Study of Zagtouli PV Power Plant

Solar Photovoltaic Power Output Forecasting using Deep Learning Models: A Case Study of Zagtouli PV Power Plant

Short-Term Forecast of Photovoltaic Solar Energy Production Using LSTM

Lightweight RepVGG-Based Cross-Modality Data Prediction Method for Solid Rocket Motors

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