Solar Power Prediction Using Dual Stream CNN-LSTM Architecture

Alharkan, Hamad; Habib, Sami J.; Islam, Muhammad

doi:10.3390/s23020945

Cited by 28 publications

(12 citation statements)

References 56 publications

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“…This underscores the advantages of incorporating diverse data features in PV power production forecasting, emphasizing the efficacy of hybrid approaches for achieving enhanced predictive performance. In a prominent study, Alharkan et al [20] introduced a novel deep learning framework (DSCLANet), which integrates a dual-stream CNN-LSTM network with a self-attention mechanism. This model employs LSTM to extract temporal dynamics and CNN for spatial pattern recognition.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhanced Forecasting Accuracy of a Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and a CNN-LSTM Model

Boucetta,

Amrane,

Chouder

et al. 2024

Energies

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Renewable energies have become pivotal in the global energy landscape. Their adoption is crucial for phasing out fossil fuels and promoting environmentally friendly energy solutions. In recent years, the energy management system (EMS) concept has emerged to manage the power grid. EMS optimizes electric grid operations through advanced metering, automation, and communication technologies. A critical component of EMS is power forecasting, which facilitates precise energy grid scheduling. This research paper introduces a deep learning hybrid model employing convolutional neural network–long short-term memory (CNN-LSTM) for short-term photovoltaic (PV) solar energy forecasting. The proposed method integrates the variational mode decomposition (VMD) algorithm with the CNN-LSTM model to predict PV power output from a solar farm in Boussada, Algeria, spanning 1 January 2019, to 31 December 2020. The performance of the developed model is benchmarked against other deep learning models across various time horizons (15, 30, and 60 min): variational mode decomposition–convolutional neural network (VMD-CNN), variational mode decomposition–long short-term memory (VMD-LSTM), and convolutional neural network–long short-term memory (CNN-LSTM), which provide a comprehensive evaluation. Our findings demonstrate that the developed model outperforms other methods, offering promising results in solar power forecasting. This research contributes to the primary goal of enhancing EMS by providing accurate solar energy forecasts.

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Section: Literature Reviewmentioning

confidence: 99%

Enhanced Forecasting Accuracy of a Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and a CNN-LSTM Model

Boucetta,

Amrane,

Chouder

et al. 2024

Energies

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“…Based on the regression analysis, it is evident that solar radiation (SR) and temperature have a positive effect on EG production, while relative humidity (RH) and vapor pressure deficit (VPD) have a negative impact (Table 7). Consequently, it becomes possible to calculate the amount of EG (in kW.h) for each station using Equations ( 15)- (20).…”

Section: Correlation Analysis: Energy Generation and Climate Variablesmentioning

confidence: 99%

Geographic information system‐based prediction of solar power plant production using deep neural networks

Mokarram

Aghaei

Mokarram

et al. 2023

IET Renewable Power Gen

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The study aims to predict solar energy generation to ensure the successful operation of solar power plants. This objective is crucial in light of the increasing energy demand, global warming concerns, and greenhouse gas emissions. To achieve this, the study employs multiple linear regression and feature selection techniques to calculate energy generation. Additionally, long short‐term memory (LSTM) is used to predict energy generation levels based on climate conditions. Furthermore, the spatial distribution of energy generation is analyzed using inverse distance weighting. The results of the study reveal that temperature, solar radiation, relative humidity, wind speed, wind direction, and vapor pressure deficit are the most significant parameters for predicting energy generation. The LSTM method proves to be highly accurate in predicting fluctuating energy generation patterns. Notably, the southern regions of the study area exhibit a greater potential for energy generation compared to the northern regions. Approximately 30% of the region generates over 1400 kWh, with the southern areas, characterized by hot and dry climates, producing around 1500 kWh, while the northern regions, with cold and humid climates, generate approximately 1100 kWh.

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“…Using historical weather data as input, a dual-stream CNN-LSTM (DSCLANet) model is employed to derive solar power predictions [ 18 ]. Comparing the DSCLANet to other models, such as LSTM-CNN, DenseNet, ELM, WPD-LSTM, and RCC-LSTM, the authors find that the DSCLANet obtains the best performance.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As a result, an IoT platform can be employed for PV plant real-time monitoring, including fault detection and prediction of the next day’s power generation. Multiple researchers propose an IoT platform for PV plant monitoring [ 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. However, they all consider monitoring a single PV plant with a single IoT platform.…”

Section: Introductionmentioning

confidence: 99%

Intelligent IoT Platform for Multiple PV Plant Monitoring

Utama

Pamungkas

Faridh

et al. 2023

Sensors

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Due to the accelerated growth of the PV plant industry, multiple PV plants are being constructed in various locations. It is difficult to operate and maintain multiple PV plants in diverse locations. Consequently, a method for monitoring multiple PV plants on a single platform is required to satisfy the current industrial demand for monitoring multiple PV plants on a single platform. This work proposes a method to perform multiple PV plant monitoring using an IoT platform. Next-day power generation prediction and real-time anomaly detection are also proposed to enhance the developed IoT platform. From the results, an IoT platform is realized to monitor multiple PV plants, where the next day’s power generation prediction is made using five types of AI models, and an adaptive threshold isolation forest is utilized to perform sensor anomaly detection in each PV plant. Among five developed AI models for power generation prediction, BiLSTM became the best model with the best MSE, MAPE, MAE, and R2 values of 0.0072, 0.1982, 0.0542, and 0.9664, respectively. Meanwhile, the proposed adaptive threshold isolation forest achieves the best performance when detecting anomalies in the sensor of the PV plant, with the highest precision of 0.9517.

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Solar Power Prediction Using Dual Stream CNN-LSTM Architecture

Cited by 28 publications

References 56 publications

Enhanced Forecasting Accuracy of a Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and a CNN-LSTM Model

Enhanced Forecasting Accuracy of a Grid-Connected Photovoltaic Power Plant: A Novel Approach Using Hybrid Variational Mode Decomposition and a CNN-LSTM Model

Geographic information system‐based prediction of solar power plant production using deep neural networks

Intelligent IoT Platform for Multiple PV Plant Monitoring

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