Review of Energy-Related Machine Learning Applications in Drying Processes

Đaković, Damir; Kljajić, Miroslav; Milivojević, Nikola; Doder, Đorđije; Anđelković, Aleksandar S.

doi:10.3390/en17010224

Cited by 3 publications

(4 citation statements)

References 80 publications

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“…They developed a multi-layered artificial feed-forward type perceptron neural network to predict drying time, head rice yield, white index, water absorption index, and elongation index based on fluidization speed and temperature [19]. Ðaković et al (2024) reviewed the potential of machine learning algorithms in improving energy and operational efficiency in drying processes [20]. Their findings suggest that these algorithms hold promise for more balanced and economic drying practices.…”

Section: Literature Reviewmentioning

confidence: 99%

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Comparative Analysis of Machine Learning Methods for Predicting Energy Recovery from Waste

Kulisz,

Kujawska,

Cioch

et al. 2024

Applied Sciences

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In the context of escalating energy demands and the quest for sustainable waste management solutions, this paper evaluates the efficacy of three machine learning methods—ElasticNet, Decision Trees, and Neural Networks—in predicting energy recovery from municipal waste across the European Union. As renewable energy sources increasingly dominate the energy production landscape, the integration of Waste-to-Energy (WTE) processes presents a dual advantage: enhancing waste management and contributing to the renewable energy mix. This study leverages a dataset incorporating economic and environmental indicators from 25 European countries, spanning 2013–2020, to compare the predictive capabilities of the three machine learning models. The analysis reveals that Neural Networks, with their intricate pattern recognition capabilities, outperform ElasticNet and Decision Trees in predicting energy recovery metrics, as evidenced by superior performance in key statistical indicators such as R-value, Mean Squared Error (MSE), and Mean Absolute Error (MAE). The comparative analysis not only demonstrates the effectiveness of each method but also suggests Neural Networks as a pivotal tool for informed decision-making in waste management and energy policy formulation. Through this investigation, the paper contributes to the sustainable energy and waste management discourse, emphasizing the critical intersection of advanced technologies, policy considerations, and environmental stewardship in addressing contemporary energy challenges.

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Section: Literature Reviewmentioning

confidence: 99%

“…Their findings suggest that these algorithms hold promise for more balanced and economic drying practices. The use of machine learning models enables precise control of drying processes and identification of areas for energy savings, resulting in reduced energy consumption and operating costs [20].…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Machine Learning Methods for Predicting Energy Recovery from Waste

Kulisz,

Kujawska,

Cioch

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Miraftabzadeh et al in [21] present a framework based on transfer learning and deep neural networks for the prediction of day-ahead photovoltaic power. Dakovic et al in [22] report an extensive review of machine learning applications aimed at addressing energy-related issues through the examination of various energy types and opportunities for energy reduction. Vontzos et al in [23] propose a data-driven short-term forecasting method for electricity consumption in airports.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Sequence-to-Sequence Deep Transfer Learning for Day-Ahead Electricity Load Forecasting

Laitsos,

Vontzos,

Tsiovoulos

et al. 2024

Electronics

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Electricity load forecasting is a crucial undertaking within all the deregulated markets globally. Among the research challenges on a global scale, the investigation of deep transfer learning (DTL) in the field of electricity load forecasting represents a fundamental effort that can inform artificial intelligence applications in general. In this paper, a comprehensive study is reported regarding day-ahead electricity load forecasting. For this purpose, three sequence-to-sequence (Seq2seq) deep learning (DL) models are used, namely the multilayer perceptron (MLP), the convolutional neural network (CNN) and the ensemble learning model (ELM), which consists of the weighted combination of the outputs of MLP and CNN models. Also, the study focuses on the development of different forecasting strategies based on DTL, emphasizing the way the datasets are trained and fine-tuned for higher forecasting accuracy. In order to implement the forecasting strategies using deep learning models, load datasets from three Greek islands, Rhodes, Lesvos, and Chios, are used. The main purpose is to apply DTL for day-ahead predictions (1–24 h) for each month of the year for the Chios dataset after training and fine-tuning the models using the datasets of the three islands in various combinations. Four DTL strategies are illustrated. In the first strategy (DTL Case 1), each of the three DL models is trained using only the Lesvos dataset, while fine-tuning is performed on the dataset of Chios island, in order to create day-ahead predictions for the Chios load. In the second strategy (DTL Case 2), data from both Lesvos and Rhodes concurrently are used for the DL model training period, and fine-tuning is performed on the data from Chios. The third DTL strategy (DTL Case 3) involves the training of the DL models using the Lesvos dataset, and the testing period is performed directly on the Chios dataset without fine-tuning. The fourth strategy is a multi-task deep learning (MTDL) approach, which has been extensively studied in recent years. In MTDL, the three DL models are trained simultaneously on all three datasets and the final predictions are made on the unknown part of the dataset of Chios. The results obtained demonstrate that DTL can be applied with high efficiency for day-ahead load forecasting. Specifically, DTL Case 1 and 2 outperformed MTDL in terms of load prediction accuracy. Regarding the DL models, all three exhibit very high prediction accuracy, especially in the two cases with fine-tuning. The ELM excels compared to the single models. More specifically, for conducting day-ahead predictions, it is concluded that the MLP model presents the best monthly forecasts with MAPE values of 6.24% and 6.01% for the first two cases, the CNN model presents the best monthly forecasts with MAPE values of 5.57% and 5.60%, respectively, and the ELM model achieves the best monthly forecasts with MAPE values of 5.29% and 5.31%, respectively, indicating the very high accuracy it can achieve.

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“…The optimization of solar air heaters in different applications can be achieved by integrating machine learning approaches such as ANN. Dakovic et al [196] discussed the different machine learning techniques to optimize the energy efficiency in the solar air drying field. 10.…”

mentioning

confidence: 99%

A Comprehensive Review of the Thermohydraulic Improvement Potentials in Solar Air Heaters through an Energy and Exergy Analysis

Hassan,

Nikbakht,

Fawzia

et al. 2024

Energies

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Supply disruptions, uncertainty, and unprecedented price rises of fossil fuels due to the recent pandemic and war have highlighted the importance of using renewable sources to meet energy demands. Solar air collectors (SACs) are major types of solar energy systems that can be utilized for space and water heating, drying, and thermal energy storage. Although there is sufficient documentation on the thermal analyses of SACs, no comprehensive reviews of the exergetic performance or qualitative insight on heat conversion are available. The primary objective of this article is to provide a comprehensive review on the optimum conditions at which the thermal performance of diverse types of solar air collectors is optimized. The effect of operating parameters such as temperature rise, flow rate, geometric parameters, solar radiation, and the Reynolds number on the thermal performance of SACs in terms of thermal hydraulic performance, energy, and exergy efficiencies has been reviewed adaptively. Beyond the operating parameters, a deep investigation is outlined to monitor fluid dynamics using analytical and computational fluid dynamics (CFDs) methodologies in the technology of SACs. In the third phase, thermodynamic irreversibility due to optical losses, thermal losses between absorber and environment, heat losses due to insulation, edge losses, and entropy generation are reported and discussed, which serve as the fundamental tools for optimization purposes.

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Review of Energy-Related Machine Learning Applications in Drying Processes

Cited by 3 publications

References 80 publications

Comparative Analysis of Machine Learning Methods for Predicting Energy Recovery from Waste

Comparative Analysis of Machine Learning Methods for Predicting Energy Recovery from Waste

Enhanced Sequence-to-Sequence Deep Transfer Learning for Day-Ahead Electricity Load Forecasting

A Comprehensive Review of the Thermohydraulic Improvement Potentials in Solar Air Heaters through an Energy and Exergy Analysis

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