Utilization of ANN and ANFIS models to predict variable speed scroll compressor with vapor injection

Zendehboudi, Alireza; Li, Xianting; Wang, Baolong

doi:10.1016/j.ijrefrig.2016.11.011

Cited by 35 publications

(8 citation statements)

References 23 publications

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“…The ANN is one of the machine learning models inspired by simulating physical signals and the neuron system of the human brain. The ANN model allows to solve physical phenomena or engineering problems without a clear equation [ [31] , [32] , [33] , [34] , [35] , [36] , [37] ]. Fig.…”

Section: Predictive Analysis Of the Impact Of Covid-19 In Dhw Demandmentioning

confidence: 99%

Analytical study on changes in domestic hot water use caused by COVID-19 pandemic

Kim

Yim

Lee

2021

Energy

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COVID-19 made considerable changes in the lifestyle of people, which have led to a rise in energy use in homes. So, this study investigated the relationship between COVID-19 and domestic hot water demands. For this purpose, a nondimensional and principal component analysis were conducted to find out the influencing factors using demand data before and after COVID-19 from our study site. Analysis showed that the COVID-19 outbreak affected the daily peak time and the amount of domestic hot water usage, the active case number of COVID-19 was a good indicator for correlating the changes in hot water demand and patterns. Based on this, a machine learning model with an artificial neural network was developed to predict hot water demand depending on the severity of COVID-19 and the relevant correlation was also derived. The model analysis showed that the increase in the number of active cases in the region affected the hot water demand increased at a certain rate and the maximum demand peak in morning during weekdays and weekends decreased. Furthermore, if the number of active cases reached more than 4000, the peak in morning moved to afternoon so that the energy use patterns of weekdays and weekends are assimilated.

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Section: Predictive Analysis Of the Impact Of Covid-19 In Dhw Demandmentioning

confidence: 99%

Analytical study on changes in domestic hot water use caused by COVID-19 pandemic

Kim

Yim

Lee

2021

Energy

View full text Add to dashboard Cite

show abstract

“…Some competitive prediction models were selected to compare outputs of the proposed model and analysis of the accuracy. Adaptive Neuro-Fuzzy Inference Systems (ANFIS) [137][138][139] and a set of classical well-known neural network-based techniques such as: Radial Basis Function Neural Network (RBF) [140,141], Multi-Layered Perceptron (MLP) [18,142], and Generalized Regression Neural Network (GRNN) [143][144][145] are nominated and optimized (through trial and error processes to minimize forecast errors) to prove the accuracy of the proposed DmGNn model through a comparison study.…”

Section: Outputs and Resultsmentioning

confidence: 99%

Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand

et al. 2019

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Recently, the natural gas (NG) global market attracted much attention as it is cleaner than oil and, simultaneously in most regions, is cheaper than renewable energy sources. However, price fluctuations, environmental concerns, technological development, emerging unconventional resources, energy security challenges, and shipment are some of the forces made the NG market more dynamic and complex. From a policy-making perspective, it is vital to uncover demand-side future trends. This paper proposed an intelligent forecasting model to forecast NG global demand, however investigating a multi-dimensional purified input vector. The model starts with a data mining (DM) step to purify input features, identify the best time lags, and pre-processing selected input vector. Then a hybrid artificial neural network (ANN) which is equipped with genetic optimizer is applied to set up ANN’s characteristics. Among 13 available input features, six features (e.g., Alternative and Nuclear Energy, CO2 Emissions, GDP per Capita, Urban Population, Natural Gas Production, Oil Consumption) were selected as the most relevant feature via the DM step. Then, the hybrid learning prediction model is designed to extrapolate the consumption of future trends. The proposed model overcomes competitive models refer to different five error based evaluation statistics consist of R2, MAE, MAPE, MBE, and RMSE. In addition, as the model proposed the best input feature set, results compared to the model which used the raw input set, with no DM purification process. The comparison showed that DmGNn overcame dramatically a simple GNn. Also, a looser prediction model, such as a generalized neural network with purified input features obtained a larger R2 indicator (=0.9864) than the GNn (=0.9679).

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“…In each layer, the nodes are divided into two forms of adaptable and fixed. In this system, the nodes of layers 2, 3, and 5 (circular nodes) signify fixed nodes, and the nodes of layers 1 and 4 (square nodes), known as adaptive nodes, represent nodes in which parameters are capable to learn [52].…”

Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

Feasibility of ANFIS-PSO and ANFIS-GA Models in Predicting Thermophysical Properties of Al2O3-MWCNT/Oil Hybrid Nanofluid

et al. 2019

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The main purpose of the present paper is to improve the performance of the adaptive neuro-fuzzy inference system (ANFIS) in predicting the thermophysical properties of Al2O3-MWCNT/thermal oil hybrid nanofluid through mixing using metaheuristic optimization techniques. A literature survey showed that the use of an artificial neural network (ANN) is the most widely used method, although there are other methods that showed better performance. Moreover, it was found in the literature that artificial intelligence methods have been widely used for predicting the thermal conductivity of nanofluids. Thus, in the present study, genetic algorithms (GAs) and particle swarm optimization (PSO) have been utilized to search and determine the antecedent and consequent parameters of the ANFIS model. Solid concentration and temperature were considered as input variables, and thermal conductivity, dynamic viscosity, heat transfer performance, and pumping power in both the internal laminar and turbulent flow regimes were the outputs. In order to evaluate and compare the performance of the models, two statistical indices of root mean square error (RMSE) and determination coefficient (R) were utilized. Based on the results, both of the models are able to predict the thermophysical properties appropriately. However, the ANFIS-PSO model had a better performance than the ANFIS-GA model. Finally, the studied thermophysical properties were developed by the trained ANFIS-PSO model.

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Utilization of ANN and ANFIS models to predict variable speed scroll compressor with vapor injection

Cited by 35 publications

References 23 publications

Analytical study on changes in domestic hot water use caused by COVID-19 pandemic

Analytical study on changes in domestic hot water use caused by COVID-19 pandemic

Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand

Feasibility of ANFIS-PSO and ANFIS-GA Models in Predicting Thermophysical Properties of Al2O3-MWCNT/Oil Hybrid Nanofluid

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