Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

Li, Hao; Liu, Zhijian; Liu, Kejun; Zhang, Zhien

doi:10.1155/2017/4194251

Cited by 56 publications

(43 citation statements)

References 55 publications

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“…In the following content, details about this screening process will be introduced. Li, Liu, et al, 2017). Being similar to a previous computational HTS concept proposed by Aspuru-Guzik and his colleagues (Pyzer-Knapp, Suh, Gómez-Bombarelli, Aguilera-Iparraguirre, & Aspuru-Guzik, 2015), a modified HTS process for the optimization in this case has been proposed, as shown in Figure 9.…”

Section: Optimizing the Thermal Performance Via An Hts Strategymentioning

confidence: 69%

Performance Prediction and Optimization of Solar Water Heater via a Knowledge-Based Machine Learning Method

Liu

2018

Handbook of Research on Power and Energy System Optimization

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Measuring the performance of solar energy and heat transfer systems requires a lot of time, economic cost and manpower. Meanwhile, directly predicting their performance is challenging due to the complicated internal structures. Fortunately, a knowledge-based machine learning method can provide a promising prediction and optimization strategy for the performance of energy systems. In this Chapter, the authors will show how they utilize the machine learning models trained from a large experimental database to perform precise prediction and optimization on a solar water heater (SWH) system. A new energy system optimization strategy based on a high-throughput screening (HTS) process is proposed. This Chapter consists of: i) Comparative studies on varieties of machine learning models (artificial neural networks (ANNs), support vector machine (SVM) and extreme learning machine (ELM)) to predict the performances of SWHs; ii) Development of an ANN-based software to assist the quick prediction and iii) Introduction of a computational HTS method to design a high-performance SWH system.

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Section: Optimizing the Thermal Performance Via An Hts Strategymentioning

confidence: 69%

Performance Prediction and Optimization of Solar Water Heater via a Knowledge-Based Machine Learning Method

Liu

2018

Handbook of Research on Power and Energy System Optimization

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“…A review of the literature shows that ANN and SVM have been used in other applications demonstrating the general acceptance of these techniques in different applications of classification tasks (Liu et al, 2018; Li et al, 2017). Therefore, in the present study, ANN and SVM, as the most popular and successful (Sammut & Webb, 2011) methods of machine learning, were also selected for sleep scoring.…”

Section: Methodsmentioning

confidence: 99%

Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis

Savareh

Bashiri

Behmanesh

et al. 2018

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IntroductionSleep scoring is an important step in the treatment of sleep disorders. Manual annotation of sleep stages is time-consuming and experience-relevant and, therefore, needs to be done using machine learning techniques.MethodsSleep-EDF polysomnography was used in this study as a dataset. Support vector machines and artificial neural network performance were compared in sleep scoring using wavelet tree features and neighborhood component analysis.ResultsNeighboring component analysis as a combination of linear and non-linear feature selection method had a substantial role in feature dimension reduction. Artificial neural network and support vector machine achieved 90.30% and 89.93% accuracy, respectively.Discussion and ConclusionSimilar to the state of the art performance, the introduced method in the present study achieved an acceptable performance in sleep scoring. Furthermore, its performance can be enhanced using a technique combined with other techniques in feature generation and dimension reduction. It is hoped that, in the future, intelligent techniques can be used in the process of diagnosing and treating sleep disorders.

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“…For the generations of new input combinations, GA is the most popular and (so far) the most successful strategy for input generation with less time-consumption. It is expected that in addition to the GA method, a machine learning-assisted HTS can be more sufficient for the generation of inputs in future study [37,104]. (3) In terms of the theoretical catalysis study, ANN has proven to be a good tool for catalytic descriptor prediction (e.g., binding energy of adsorbate on a catalytic surface).…”

Section: Remarks and Prospectsmentioning

confidence: 99%

“…All the variables only pass to the same direction (from left to right). Reproduced with permission from Reference [37].…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Networks for Catalysis: A Review

2017

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Abstract:Machine learning has proven to be a powerful technique during the past decades. Artificial neural network (ANN), as one of the most popular machine learning algorithms, has been widely applied to various areas. However, their applications for catalysis were not well-studied until recent decades. In this review, we aim to summarize the applications of ANNs for catalysis research reported in the literature. We show how this powerful technique helps people address the highly complicated problems and accelerate the progress of the catalysis community. From the perspectives of both experiment and theory, this review shows how ANNs can be effectively applied for catalysis prediction, the design of new catalysts, and the understanding of catalytic structures.

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Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

Cited by 56 publications

References 55 publications

Performance Prediction and Optimization of Solar Water Heater via a Knowledge-Based Machine Learning Method

Performance Prediction and Optimization of Solar Water Heater via a Knowledge-Based Machine Learning Method

Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis

Application of Artificial Neural Networks for Catalysis: A Review

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