Assessment of Deep Learning techniques for Prognosis of solar thermal systems

Correa-Jullian, Camila; Cardemil, José M.; Droguett, Enrique López; Behzad, Masoud

doi:10.1016/j.renene.2019.07.100

Cited by 66 publications

(20 citation statements)

References 33 publications

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“…Fortunately, Deep learning (DL) methods are promising approaches for learning the intrinsic non-linear characteristics and constant data patterns [43], [44]. DL methods are highly accurate energy forecasting models for modelling energy demand and supply due to their high performance in dealing with solid data regularity, and periodicity [14], [45]- [47]. In addition, DL methods are reliable for learning long-term dependencies of energy data, leading to accurate forecasting results.…”

Section: A Forecasting Methodsmentioning

confidence: 99%

Deep Learning in Energy Modeling: Application in Smart Buildings With Distributed Energy Generation

et al. 2021

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Over 33% of final energy consumption is used in buildings which leads to nearly 40% of total direct and indirect CO 2 emissions in the world. While energy consumption is steadily rising globally, managing building energy utilization by on-site renewable energy generation can help responding this demand. This paper proposes a deep learning method based on a discrete wavelet transformation and long short-term memory method (DWT-LSTM) and a scheduling framework for the integrated modelling and management of energy demand and supply for buildings. This method considers several factors including a two-step electricity price, uncertainty in climatic factors, availability of renewable energy resources (wind and solar), energy consumption patterns in buildings, and the non-linear relationships between these parameters. This novel method analyzes and continuously learns from data patterns based on hourly, daily, weekly and monthly intervals. The method enables monitoring and controlling renewable energy generation, the share of energy imports from the grid, employment of saving strategy based on the user priority list, and energy storage management. The main objective of the proposed method is to minimize the reliance on the grid and electricity cost, especially during the peak days. The results demonstrate that the proposed method can forecast building energy demand and energy supply with a high level of accuracy, showing a 3.63-8.57% error range in hourly data prediction for one month ahead. The results also show that the method can supply 304 days (83.2%) of a year without reliance on energy grids, decreasing 87.2% in energy demand on one hand and exporting annually 7777 kWh to the grid on the other hand. In addition, the rescheduling framework decreased the imported electricity cost with the higher electricity tariff by 98 %. The combination of the deep learning forecasting, energy storage, and scheduling algorithm enables reducing annual energy import from the grid from 6709 to 858 kWh (84.3%).

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Section: A Forecasting Methodsmentioning

confidence: 99%

Deep Learning in Energy Modeling: Application in Smart Buildings With Distributed Energy Generation

et al. 2021

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“…Torres et al [42] proposed an FFNN to predict the day ahead electricity generated by PV solar systems, while Kamadinata et al [43] forecasted the solar radiation from sky images using the ANN architecture. Similarly, Correa-Jullian et al [44] explored the techniques of ANN, RNN, and LSTM and found these methods reliable for solar energy prediction. AlKandari et al [45] used both ML and statistical methods for the prediction of future solar power generation in solar plants.…”

Section: Solar Power Generationmentioning

confidence: 99%

AB-Net: A Novel Deep Learning Assisted Framework for Renewable Energy Generation Forecasting

et al. 2021

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Renewable energy (RE) power plants are deployed globally because the renewable energy sources (RESs) are sustainable, clean, and environmentally friendly. However, the demand for power increases on a daily basis due to population growth, technology, marketing, and the number of installed industries. This challenge has raised a critical issue of how to intelligently match the power generation with the consumption for efficient energy management. To handle this issue, we propose a novel architecture called ‘AB-Net’: a one-step forecast of RE generation for short-term horizons by incorporating an autoencoder (AE) with bidirectional long short-term memory (BiLSTM). Firstly, the data acquisition step is applied, where the data are acquired from various RESs such as wind and solar. The second step performs deep preprocessing of the acquired data via several de-noising and cleansing filters to clean the data and normalize them prior to actual processing. Thirdly, an AE is employed to extract the discriminative features from the cleaned data sequence through its encoder part. BiLSTM is used to learn these features to provide a final forecast of power generation. The proposed AB-Net was evaluated using two publicly available benchmark datasets where the proposed method obtains state-of-the-art results in terms of the error metrics.

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“…In the same field, Yang et al [34] proved that compared to vector modelling, LSTM showed a higher predictive accuracy, faster response time, and stronger generalization capability. In the case of renewable resources, Correa-Jullian et al [36] compared the predictive methods based on standard neural networks and LSTM. The comparison showed that LSTM models achieve the lowest RMSE (Root Mean Square Error) error score, lowest standard deviation, and smallest relative error.…”

Section: Literature Reviewmentioning

confidence: 99%

Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM

2020

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There is no doubt that the issue of making a good prediction about a company’s possible failure is very important, as well as complicated. A number of models have been created for this very purpose, of which one, the long short-term memory (LSTM) model, holds a unique position in that it generates very good results. The objective of this contribution is to create a methodology for the identification of a company failure (bankruptcy) using artificial neural networks (hereinafter referred to as “NN”) with at least one long short-term memory (LSTM) layer. A bankruptcy model was created using deep learning, for which at least one layer of LSTM was used for the construction of the NN. For the purposes of this contribution, Wolfram’s Mathematica 13 (Wolfram Research, Champaign, Illinois) software was used. The research results show that LSTM NN can be used as a tool for predicting company failure. The objective of the contribution was achieved, since the model of a NN was developed, which is able to predict the future development of a company operating in the manufacturing sector in the Czech Republic. It can be applied to small, medium-sized and manufacturing companies alike, as well as used by financial institutions, investors, or auditors as an alternative for evaluating the financial health of companies in a given field. The model is flexible and can therefore be trained according to a different dataset or environment.

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Assessment of Deep Learning techniques for Prognosis of solar thermal systems

Cited by 66 publications

References 33 publications

Deep Learning in Energy Modeling: Application in Smart Buildings With Distributed Energy Generation

Deep Learning in Energy Modeling: Application in Smart Buildings With Distributed Energy Generation

AB-Net: A Novel Deep Learning Assisted Framework for Renewable Energy Generation Forecasting

Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM

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