Data mining with 12 machine learning algorithms for predict costs and carbon dioxide emission in integrated energy-water optimization model in buildings

Javanmard, Majid Emami; Ghaderi, S.F.; Hoseinzadeh, Mohamad

doi:10.1016/j.enconman.2021.114153

Cited by 39 publications

(18 citation statements)

References 69 publications

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“…The Ridge and Lasso additionally use l2-norm and l1-norm as constraints in the existing model. These characteristics of the models show better performance than the conventional linear regression, which uses the least-squares method to find appropriate weights and biases to reduce overfitting 48 , 49 .…”

Section: Methodsmentioning

confidence: 99%

Incorporation of machine learning and deep neural network approaches into a remote sensing-integrated crop model for the simulation of rice growth

Jeong

Shin

et al. 2022

Sci Rep

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Machine learning (ML) and deep neural network (DNN) techniques are promising tools. These can advance mathematical crop modelling methodologies that can integrate these schemes into a process-based crop model capable of reproducing or simulating crop growth. In this study, an innovative hybrid approach for estimating the leaf area index (LAI) of paddy rice using climate data was developed using ML and DNN regression methodologies. First, we investigated suitable ML regressors to explore the LAI estimation of rice based on the relationship between the LAI and three climate factors in two administrative rice-growing regions of South Korea. We found that of the 10 ML regressors explored, the random forest regressor was the most effective LAI estimator, and it even outperformed the DNN regressor, with model efficiencies of 0.88 in Cheorwon and 0.82 in Paju. In addition, we demonstrated that it would be feasible to simulate the LAI using climate factors based on the integration of the ML and DNN regressors in a process-based crop model. Therefore, we assume that the advancements presented in this study can enhance crop growth and productivity monitoring practices by incorporating a crop model with ML and DNN plans.

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Section: Methodsmentioning

confidence: 99%

Incorporation of machine learning and deep neural network approaches into a remote sensing-integrated crop model for the simulation of rice growth

Jeong

Shin

et al. 2022

Sci Rep

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“…In the following, we presented a comparative study of MLA. The MLA find their applications in several areas, namely: text classification [13]- [17], medical diagnosis [18], pollution prediction [19], spam email detection [20], plant disease identification [21], and stock daily trading [22]. For example, The paper [13] describes the use of the KNN algorithm with the TF-IDF method for text classification.…”

Section: Related Workmentioning

confidence: 99%

“…They found that the model with LR and SVM works well on diabetes prediction. The authors of the study [19] tested 12 MLA to predict costs and carbon dioxide emission in an integrated energywater optimization model and considered four indices to examine the prediction accuracy of the algorithms. Meanwhile, the light gradient boosting machine and extra tree algorithms enjoyed higher prediction accuracy in Int J Artif Intell ISSN: 2252-8938…”

Section: Related Workmentioning

confidence: 99%

Modeling of an artificial intelligence based enterprise callbot with natural language processing and machine learning algorithms

Aattouri¹,

Mouncif

Rida³

2023

IJ-AI

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<p>The management of customer services by telephone encounters several problems: an uncontrollable flow of calls, complicated resource management, a very high cost of service, and more. Opportunities to improve the quality of service, save time and money triggered the widespread implementation of artificial intelligence (AI) based callbot. This article outlines the straightforward workflow developed to model the architecture of the callbot. Therefore, several algorithms were evaluated and compared based on real knowledge of a call center of an insurance society. The algorithms considered are: k-nearest neighbours (KNN), support vector machine (SVM), random forests (RF), logistic regression (LR), and Na¨ıve Bayes (NB). The comparison criteria are: correct responses, response time, accuracy, Cohen’s kappa and F1 score using n-gram (1.1) and (2.2). The results obtained show that the SVM (accuracy=70.29%) presents the best results on all the comparison criteria. The comparison between the results of the human agents and the callbot shows an improvement in several levels: the cost savings are greater than 80% on all the tests carried out, the holding time decrease to 0 seconds, and the processing time (almost a third or more). The results obtained sufficiently meet the objectives of this project.</p>

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“…SVMs are primarily used to forecast short‐term time series data and nonlinear data 24 . ANNs simulate human thinking, using neuron nodes to learn nonlinear relationships between historical data and thus solve complex problems 25 …”

Section: Literature Reviewmentioning

confidence: 99%

Metaheuristics‐optimized deep learning to predict generation of sustainable energy from rooftop plant microbial fuel cells

Chou

Cheng

Liu

et al. 2022

Intl J of Energy Research

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Plant microbial fuel cells (PMFCs) are an emergent green-energy technology that continuously converts solar energy into electricity. Placing PMFCs on the roofs of urban buildings can help to create green urban environments even as they generate power. The power generation performance of PMFCs is affected by a range of environmental factors, so their power generation capacity is difficult to estimate. To develop an artificial intelligence model to forecast PMFC power generation accurately, relevant results obtained using shallow and deep learning techniques are compared for the first time. Once deep learning techniques had been identified as superior for this purpose, they were used with a bio-inspired optimization algorithm to dynamically setting the model hyperparameters. The developed model can also be applied to estimate the power generation capacity of PMFC devices in the future. The model was trained using data collected from sensors in a site experiment that was carried out using PMFCs embedded with Chinese pennisetumin (Pennisetum alopecuroides), narrowleaf cattail (Typha angustifolia), dwarf rotala (Rotala rotundifolia), and no plant as a control group. The original data of device parameters, environmental parameters, and the measured power generation of PMFCs in numerical form were applied to train shallow learning and time-series deep learning models. Meanwhile, the state-of-the-art sliding window technique was used to establish a numerical matrix, which was converted into a 2D image-like format to represent inputs for deep convolutional neural network (CNN) models. The accuracy in predicting the power generation capacity of PMFC devices showed that EfficientNet, an advanced type of CNN, was the best model among the shallow and deep learning techniques. These analytical results demonstrate the superior performance of deep CNNs in learning image features and their consequent suitability for constructing PMFC power generation forecasting models. To enhance the generalization performance of CNN, a

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Data mining with 12 machine learning algorithms for predict costs and carbon dioxide emission in integrated energy-water optimization model in buildings

Cited by 39 publications

References 69 publications

Incorporation of machine learning and deep neural network approaches into a remote sensing-integrated crop model for the simulation of rice growth

Incorporation of machine learning and deep neural network approaches into a remote sensing-integrated crop model for the simulation of rice growth

Modeling of an artificial intelligence based enterprise callbot with natural language processing and machine learning algorithms

Metaheuristics‐optimized deep learning to predict generation of sustainable energy from rooftop plant microbial fuel cells

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