Deep Neural Networks for Multivariate Prediction of Photovoltaic Power Time Series

Succetti, Federico; Rosato, Antonello; Araneo, Rodolfo; Panella, Massimo

doi:10.1109/access.2020.3039733

Cited by 37 publications

(13 citation statements)

References 65 publications

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“…It was established that the most appropriate supervised machine learning technique would be Neural Networks, which were used in works such as [36] and other cases with Deep Neural Networks [37]. In the present study, an average prediction of 90.12% was found in an execution time of 0.02 seconds; it was also the one that best suited the type of prediction that was executed in this investigation since neural networks create their interpretation of their information inside and are more robust to fault tolerance and flexible when the input data may present changes that are not so significant.…”

Section: Discussionmentioning

confidence: 99%

Supervised Machine Learning Techniques for the Prediction of the State of Charge of Batteries in Photovoltaic Systems in the Mining Sector

Pinto¹,

Tejada²,

Lopez-Casaperalta³

et al. 2022

IEEE Access

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One of the critical aspects in the mining sector is energy, being of great importance for the operation since if it were to stop, one of the consequences would be the loss of large amounts of money. The research objective is to predict the State of Charge of Batteries of equipment powered by photovoltaic solar panels in the mining sector based on automatic supervised learning techniques. A monitoring system records each energy variable programmed in the photovoltaic system, for which an analysis of the data extracted from the monitoring system was carried out. The data were evaluated using automatic supervised learning techniques using the RapidMiner tool, whose prediction average was 90.12%. The technique of automatic supervised learning of artificial neural networks was chosen to predict the state of charge of batteries for photovoltaic systems. A software tool was built with the neural network. The analysis and discussion of the results of the training of the model were carried out, the contribution of this research being to determine the prediction of the state of charge of batteries in photovoltaic systems in the mining sector using techniques of supervised machine learning which was the neural network. Finally, with the model correctly trained, validation was carried out that allowed comparing the predictive data with the data in realtime, obtaining a good relationship and satisfactory results.

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Section: Discussionmentioning

confidence: 99%

Supervised Machine Learning Techniques for the Prediction of the State of Charge of Batteries in Photovoltaic Systems in the Mining Sector

Pinto¹,

Tejada²,

Lopez-Casaperalta³

et al. 2022

IEEE Access

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“…Tis study also utilizes LSTM model, which is a special type of RNN and is able to deal with long-term time dependencies [28]. Tere are many types of LSTM models that can be used for specifc type of time series forecasting problem.…”

Section: Arima and Lstm Modelsmentioning

confidence: 99%

Impact of Tree Cover Loss on Carbon Emission: A Learning‐Based Analysis

Butt

Jamshed

Rahman

et al. 2023

Computational Intelligence and Neuroscience

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Describing the processes leading to deforestation is essential for the development and implementation of the forest policies. In this work, two different learning models were developed in order to identify the best possible model for the assessment of the deforestation causes and trends. We developed autoregressive integrated moving average (ARIMA) model and long short-term memory (LSTM) independently in order to see the trend between tree cover loss and carbon dioxide emission. This study includes the twenty-year data of Pakistan on tree cover loss and carbon emission from the Global Forest Watch (GFW) platform, a known platform to get numerical data. Minimum mean absolute error (MAE) for the prediction of tree cover loss and carbon emission obtained through ARIMA model is 0.89 and 0.95, respectively. The minimum MAE given by LSTM model is 0.33 and 0.43, respectively. There is no such kind of study conducted in order to identify the increase in carbon emission due to tree cover loss most specifically in Pakistan. The results endorsed that one of the main causes of increase in the pollution in the environment in terms of carbon emission is due to tree cover loss.

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“…Recently, methodologies based on data analysis and information extraction, in the broad field of machine learning, are being increasingly used to address damage/failure identification problems to achieve a wider range of applicability [ 37 , 38 ]. In order to overcome the limitations associated to traditional neural networks solutions [ 39 ], such as real-world noise, more complex deep learning (DL) models and techniques, with higher generalisation capabilities, have been introduced as data extractors, classifiers, and predictors [ 40 , 41 , 42 ]. Such models can include also recurrent neural networks (RNN) [ 43 ] to efficiently obtain the information from time-series data.…”

Section: Introductionmentioning

confidence: 99%

A Study on Structural Health Monitoring of a Large Space Antenna via Distributed Sensors and Deep Learning

Angeletti

Iannelli

Gasbarri

et al. 2022

Sensors

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Most modern Earth and Universe observation spacecraft are now equipped with large lightweight and flexible structures, such as antennas, telescopes, and extendable elements. The trend of hosting more complex and bigger appendages, essential for high-precision scientific applications, made orbiting satellites more susceptible to performance loss or degradation due to structural damages. In this scenario, Structural Health Monitoring strategies can be used to evaluate the health status of satellite substructures. However, in particular when analysing large appendages, traditional approaches may not be sufficient to identify local damages, as they will generally induce less observable changes in the system dynamics yet cause a relevant loss of payload data and information. This paper proposes a deep neural network to detect failures and investigate sensor sensitivity to damage classification for an orbiting satellite hosting a distributed network of accelerometers on a large mesh reflector antenna. The sensors-acquired time series are generated by using a fully coupled 3D simulator of the in-orbit attitude behaviour of a flexible satellite, whose appendages are modelled by using finite element techniques. The machine learning architecture is then trained and tested by using the sensors’ responses gathered in a composite scenario, including not only the complete failure of a structural element (structural break) but also an intermediate level of structural damage. The proposed deep learning framework and sensors configuration proved to accurately detect failures in the most critical area or the structure while opening new investigation possibilities regarding geometrical properties and sensor distribution.

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Deep Neural Networks for Multivariate Prediction of Photovoltaic Power Time Series

Cited by 37 publications

References 65 publications

Supervised Machine Learning Techniques for the Prediction of the State of Charge of Batteries in Photovoltaic Systems in the Mining Sector

Supervised Machine Learning Techniques for the Prediction of the State of Charge of Batteries in Photovoltaic Systems in the Mining Sector

Impact of Tree Cover Loss on Carbon Emission: A Learning‐Based Analysis

A Study on Structural Health Monitoring of a Large Space Antenna via Distributed Sensors and Deep Learning

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