PV Power Prediction, Using CNN-LSTM Hybrid Neural Network Model. Case of Study: Temixco-Morelos, México

Tovar, Mario; Robles, Miguel; Rashid, Felipe

doi:10.3390/en13246512

Cited by 53 publications

(35 citation statements)

References 32 publications

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“…The proposed model provides a high versatility to deal with the nonlinear behaviors to provide an accurate PV output. A CNN-LSTM model has been proposed for PVPF [89]. However, the extraction of positional and temporal representation in the PV output requires explicit recognition of patterns and regularities in data, challenging to compute due to the massive computational burden in real-life application.…”

Section: Hybrid Models-basedmentioning

confidence: 99%

Convergence of Photovoltaic Power Forecasting and Deep Learning: State-of-Art Review

et al. 2021

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Deep learning (DL)-based PV Power Forecasting (PVPF) emerged nowadays as a promising research direction to intelligentize energy systems. With the massive smart meter integration, DL takes advantage of the large-scale and multi-source data representations to achieve a spectacular performance and high PV forecastability potential compared to classical models. This review article taxonomically dives into the nitty-gritty of the mainstream DL-based PVPF methods while showcasing their strengths and weaknesses. Firstly, we draw connections between PVPF and DL approaches and show how this relation might cross-fertilize or extend both directions. Then, fruitful discussions are conducted based on three classes: discriminative learning, generative learning, and deep reinforcement learning. In addition, this review analyzes recent automatic architecture optimization algorithms for DL-based PVPF. Next, the notable DL technologies are thoroughly described. These technologies include federated learning, deep transfer learning, incremental learning, and big data DL. After that, DL methods are taxonomized into deterministic and probabilistic PVPF. Finally, this review concludes with some research gaps and hints about future challenges and research directions in driving the further success of DL techniques to PVPF applications. By compiling this study, we expect to help aspiring stakeholders widen their knowledge of the staggering potential of DL for PVPF.

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Section: Hybrid Models-basedmentioning

confidence: 99%

Convergence of Photovoltaic Power Forecasting and Deep Learning: State-of-Art Review

et al. 2021

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“…Considering the potential, deep learning has been applied in various domains and applications for different purposes [ Many researchers exploited the convolutional aspect of CNN in combination with LSTM to improve the performance of time-series prediction/forecasting in various applications, such as for inventory prediction [30], stock price prediction [63] [64] [65] [66], gold price forecasting [28], Bitcoin price forecasting [67], tourist flow forecasting [68], sentiment prediction of social media users [69], household power consumption prediction [70] [27], photovoltaic power prediction [71], wind power forecasting [72], PM2.5 prediction [73] [74], predicting NOx emission in processing of heavy oil [75], forecasting natural gas price and movement [29], urban expansion prediction [76], predicting waterworks operations at a water purification plant [77], predicting sea surface temperature [78], typhoon formation forecasting [79], crop yield prediction [80], COVID-19 detection and predictions [81] [82] [83], human age estimation [84], and so on. [106] used LSTM to predict the availability of mobile edge computing-enabled base stations depending on the vehicle's mobility for offloading the computation jobs from the vehicle to the base station.…”

Section: B Deep Learning For Resource Management and Predictionmentioning

confidence: 99%

Deep Learning Based Resource Availability Prediction for Local Mobile Crowd Computing

et al. 2021

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Mobile crowd computing (MCC) that utilizes public-owned (crowd's) smart mobile devices (SMDs) collectively can give adequate computing power without any additional financial and ecological cost. However, the major challenge is to cope with the mobility (or availability) issue of SMDs. User's unpredicted mobility makes the SMDs really unstable resources. Selecting such erratic resources for job schedule would result in frequent job offloading and, in the worst case, job loss, which would affect the overall performance and the quality of service of MCC. In a Local MCC, generally, a set of users are available for a certain period regularly. Based on this information, the chances of a user being available for a certain duration from a given point of time can be predicted. In this paper, we provide an effective model to predict the availability of the users (i.e., their SMDs) in such an MCC environment. We argue that before submitting a job to an SMD, the stability of it is to be assessed for the duration of execution of the job to be assigned. If the predicted availability time is greater than the job size, then only the job should be assigned to the SMD. An accurate prediction will minimize the unnecessary job offloading or job loss due to the early departure of the designated SMD. We propose an advanced convolutional feature extraction mechanism that is applied to LSTM and GRU-based time-series prediction models for predicting SMD availability. To collect user mobility data, we considered a research lab scenario, where real mobility traces were recorded with respect to a Wi-Fi access point. We compared the prediction performances of convolutional LSTM and GRU with the basic LSTM and GRU and ARIMA in terms of MAE, RMSE, R 2 , accuracy, and perplexity. In all the measurements, the proposed convolutional LSTM exhibits considerably better prediction performance.

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“…Li et al [21] also combined the SAE-LSTM with wavelet packet decomposition (WPD) to examine and validate the anomaly detection performance in a rotating machine. In [22], Tovar et al proposed a hybrid model of CNN-LSTM (convolutional neural network) for predictions of PV power, where the CNN method was used to extract and select the local features, while LSTM was used for extracting the temporal features of the real data in Mexico Temixco. The hybrid model of CNN-LSTM introduced by [22] has better prediction than the single prediction model.…”

Section: Introductionmentioning

confidence: 99%

“…In [22], Tovar et al proposed a hybrid model of CNN-LSTM (convolutional neural network) for predictions of PV power, where the CNN method was used to extract and select the local features, while LSTM was used for extracting the temporal features of the real data in Mexico Temixco. The hybrid model of CNN-LSTM introduced by [22] has better prediction than the single prediction model. In [23], Ahmadipour et al built a novel technique by combining the wavelet packet transform (WPT) and probabilistic neural network (PNN) for islanding detection in a PV system, where WPT was applied to extract the islanding feature and PNN was used for islanding detection.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Deep Learning Model for Fault Detection and Classification of Grid-Connected Photovoltaic System

et al. 2022

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Effective fault detection and classification play essential roles in reducing the hazards such as electric shocks and fire in photovoltaic (PV) systems. However, the issues of interest in fault detection and classification for PV systems remain an open-ended challenge due to manual and time-consuming processes that require the relevant domain knowledge and experience of fault diagnoses. This paper proposes a hybrid deep-learning (DL) model-based combined architectures as the novel DL approaches to achieve the real-time automatic fault detection and classification of a PV system. This research employed the wavelet packet transform (WPT) as a data preprocessing technique to handle the PV voltage signal collected and feeding them as the inputs for combined DL architectures that consist of the equilibrium optimizer algorithm (EOA) and long short-term memory (LSTM-SAE) approaches. The combined DL architectures are able to extract the fault features automatically from the preprocessed data without requiring any previous knowledge, therefore can override the traditional shortages of manual feature extraction and manual selection of optimal features from the extracted fault features. These desirable features are anticipated to speed up the fault detection and classification capability of the proposed DL model with higher accuracy. In order to determine the performance of the proposed fault model, we carried out a comprehensive evaluation study on a 250-kW grid-connected PV system. In this paper, symmetrical and asymmetrical faults have been studied involving all the phases and ground faults such as single phase to ground, phases to phase, phase to phase to ground, and three-phase to ground. The simulation results validate the efficacy of the proposed model in terms of computation time, accuracy of fault detection, and noise robustness. Comprehensive comparisons between the simulation results and previous studies demonstrate the multidisciplinary applications of the present study.INDEX TERMS Deep distributed energy, equilibrium optimizer algorithm (EOA), fault detection and classification, grid-connected photovoltaic systems, optimal feature selection, wavelet packet transform (WPT).

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PV Power Prediction, Using CNN-LSTM Hybrid Neural Network Model. Case of Study: Temixco-Morelos, México

Cited by 53 publications

References 32 publications

Convergence of Photovoltaic Power Forecasting and Deep Learning: State-of-Art Review

Convergence of Photovoltaic Power Forecasting and Deep Learning: State-of-Art Review

Deep Learning Based Resource Availability Prediction for Local Mobile Crowd Computing

Hybrid Deep Learning Model for Fault Detection and Classification of Grid-Connected Photovoltaic System

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