Extreme gradient boosting model based on improved Jaya optimizer applied to forecasting energy consumption in residential buildings

2021 ◽  
Author(s):  
João Sauer ◽  
Viviana Cocco Mariani ◽  
Leandro dos Santos Coelho ◽  
Matheus Henrique Dal Molin Ribeiro ◽  
Mirco Rampazzo
Keyword(s):  
Energy Consumption ◽  
Residential Buildings ◽  
Gradient Boosting ◽  
Model Based ◽  
Extreme Gradient Boosting

scholarly journals Machine Learning-Based Energy System Model for Tissue Paper Machines

Processes ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 655
Author(s):  
Huanhuan Zhang ◽  
Jigeng Li ◽  
Mengna Hong
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Gradient Boosting ◽  
Paper Machine ◽  
Steam Consumption ◽  
Tissue Paper ◽  
Paper Machines ◽  
Energy Consumption Model ◽  
Extreme Gradient Boosting ◽  
Consumption Model

With the global energy crisis and environmental pollution intensifying, tissue papermaking enterprises urgently need to save energy. The energy consumption model is essential for the energy saving of tissue paper machines. The energy consumption of tissue paper machine is very complicated, and the workload and difficulty of using the mechanism model to establish the energy consumption model of tissue paper machine are very large. Therefore, this article aims to build an empirical energy consumption model for tissue paper machines. The energy consumption of this model includes electricity consumption and steam consumption. Since the process parameters have a great influence on the energy consumption of the tissue paper machines, this study uses three methods: linear regression, artificial neural network and extreme gradient boosting tree to establish the relationship between process parameters and power consumption, and process parameters and steam consumption. Then, the best power consumption model and the best steam consumption model are selected from the models established by linear regression, artificial neural network and the extreme gradient boosting tree. Further, they are combined into the energy consumption model of the tissue paper machine. Finally, the models established by the three methods are evaluated. The experimental results show that using the empirical model for tissue paper machine energy consumption modeling is feasible. The result also indicates that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The experimental results show that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The mean absolute percentage error of the electricity consumption model and the steam consumption model built by the extreme gradient boosting tree is approximately 2.72 and 1.87, respectively. The root mean square errors of these two models are about 4.74 and 0.03, respectively. The result also indicates that using the empirical model for tissue paper machine energy consumption modeling is feasible, and the extreme gradient boosting tree is an efficient method for modeling energy consumption of tissue paper machines.

scholarly journals An ARDS Severity Recognition Model based on XGBoost

2021 ◽  
Vol 2138 (1) ◽  
pp. 012009
Author(s):  
Huimin Zhang ◽  
Renshuang Ding ◽  
Qi Zhang ◽  
Mingxing Fang ◽  
Guanghua Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Pearson Correlation ◽  
Area Under The Curve ◽  
Gradient Boosting ◽  
Blood Oxygen Saturation ◽  
Recognition Model ◽  
Model Based ◽  
Extreme Gradient Boosting ◽  
Mimic Iii

Abstract Given the subjectivity and non-real-time of disease scoring system and invasive parameters in evaluating the development of acute respiratory distress syndrome (ARDS), combined with noninvasive parameters, this paper proposed an ARDS severity recognition model based on extreme gradient boosting (XGBoost). Firstly, the physiological parameters of patients were extracted based on the MIMIC-III database for statistical analysis, and the outliers and unbalanced samples were processed by the interquartile range and synthetic minority oversampling technique. Then, Pearson correlation coefficient and random forest were used as hybrid feature selection to score the noninvasive parameters comprehensively, and essential parameters for identifying diseases were obtained. Finally, XGBoost combined with grid search cross-validation to determine the best hyper-parameters of the model to realize the accurate classification of disease degree. The experimental results show that the model’s area under the curve (AUC) is as high as 0.98, and the accuracy is 0.90; the total score of blood oxygen saturation (SpO2) is 0.625, which could be used as an essential parameter to evaluate the severity of ARDS. Compared with traditional methods, this model has excellent advantages in real-time and accuracy and could provide more accurate diagnosis and treatment suggestions for medical staff.

Forecasting public bicycle rental demand using an optimized eXtreme Gradient Boosting model

2021 ◽  
pp. 1-19
Author(s):  
Yuanjiao Hu ◽  
Zhaoyun Sun ◽  
Wei Li ◽  
Lili Pei
Keyword(s):  
Search Algorithm ◽  
Demand Forecasting ◽  
Forecast Model ◽  
Original Data ◽  
Gradient Boosting ◽  
Model Parameters ◽  
Grid Search ◽  
Demand Forecast ◽  
Model Based ◽  
Extreme Gradient Boosting

The rational distribution of public bicycle rental fleets is crucial for improving the efficiency of public bicycle programs. The accurate prediction of the demand for public bicycles is critical to improve bicycle utilization. To overcome the shortcomings of traditional algorithms such as low prediction accuracy and poor stability, using the 2011–2012 hourly bicycle rental data provided by the Washington City Bicycle Rental System, this study aims to develop an optimized and innovative public bicycle demand forecasting model based on grid search and eXtreme Gradient Boosting (XGBoost) algorithm. First, the feature ranking method based on machine learning models is used to analyze feature importance on the original data. In addition, a public bicycle demand forecast model is established based on important factors affecting bicycle utilization. Finally, to predict bicycle demand accurately, this study optimizes the model parameters through a grid search (GS) algorithm and builds a new prediction model based on the optimal parameters. The results show that the optimized XGBoost model based on the grid search algorithm can predict the bicycle demand more accurately than other models. The optimized model has an R-Squared of 0.947, and a root mean squared logarithmic error of 0.495. The results can be used for the effective management and reasonable dispatch of public bicycles.

A comparative performance of machine learning algorithm to predict electric vehicles energy consumption: A path towards sustainability

2021 ◽  
pp. 0958305X2110449
Author(s):  
Irfan Ullah ◽  
Kai Liu ◽  
Toshiyuki Yamamoto ◽  
Rabia Emhamed Al Mamlook ◽  
Arshad Jamal
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Electric Vehicles ◽  
Absolute Error ◽  
Gradient Boosting ◽  
Light Gradient ◽  
Gradient Boosting Machine ◽  
Extreme Gradient Boosting ◽  
Energy Consumption Prediction ◽  
Transport Emissions

The rapid growth of transportation sector and related emissions are attracting the attention of policymakers to ensure environmental sustainability. Therefore, the deriving factors of transport emissions are extremely important to comprehend. The role of electric vehicles is imperative amid rising transport emissions. Electric vehicles pave the way towards a low-carbon economy and sustainable environment. Successful deployment of electric vehicles relies heavily on energy consumption models that can predict energy consumption efficiently and reliably. Improving electric vehicles’ energy consumption efficiency will significantly help to alleviate driver anxiety and provide an essential framework for operation, planning, and management of the charging infrastructure. To tackle the challenge of electric vehicles’ energy consumption prediction, this study aims to employ advanced machine learning models, extreme gradient boosting, and light gradient boosting machine to compare with traditional machine learning models, multiple linear regression, and artificial neural network. Electric vehicles energy consumption data in the analysis were collected in Aichi Prefecture, Japan. To evaluate the performance of the prediction models, three evaluation metrics were used; coefficient of determination ( R2), root mean square error, and mean absolute error. The prediction outcome exhibits that the extreme gradient boosting and light gradient boosting machine provided better and robust results compared to multiple linear regression and artificial neural network. The models based on extreme gradient boosting and light gradient boosting machine yielded higher values of R2, lower mean absolute error, and root mean square error values have proven to be more accurate. However, the results demonstrated that the light gradient boosting machine is outperformed the extreme gradient boosting model. A detailed feature important analysis was carried out to demonstrate the impact and relative influence of different input variables on electric vehicles energy consumption prediction. The results imply that an advanced machine learning model can enhance the prediction performance of electric vehicles energy consumption.

scholarly journals A clinical diagnostic model based on an eXtreme Gradient Boosting algorithm to distinguish type 1 diabetes

2021 ◽  
Vol 9 (5) ◽  
pp. 409-409
Author(s):  
Xiaohan Tang ◽  
Rui Tang ◽  
Xingzhi Sun ◽  
Xiang Yan ◽  
Gan Huang ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Gradient Boosting ◽  
Diagnostic Model ◽  
Model Based ◽  
Clinical Diagnostic ◽  
Extreme Gradient Boosting ◽  
Boosting Algorithm

scholarly journals Short and Very Short Term Firm-level Load Forecasting for Warehouses: A Comparison of Machine Learning and Deep Learning Models

2022 ◽  
Author(s):  
Andrea Maria N. C. Ribeiro ◽  
Pedro Rafael X. do Carmo ◽  
Patricia Takako Endo ◽  
Pierangelo Rosati ◽  
Theo Lynn
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Energy Consumption ◽  
Load Forecasting ◽  
Energy Performance ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Models ◽  
Short Term ◽  
Extreme Gradient Boosting

Commercial buildings are a significant consumer of energy worldwide. Logistics facilities, and specifically warehouses, are a common building type yet under-researched in the demand-side energy forecasting literature. Warehouses have an idiosyncratic profile when compared to other commercial and industrial buildings with a significant reliance on a small number of energy systems. As such, warehouse owners and operators are increasingly entering in to energy performance contracts with energy service companies (ESCOs) to minimise environmental impact, reduce costs, and improve competitiveness. ESCOs and warehouse owners and operators require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. This paper explores the performance of three machine learning models (Support Vector Regression (SVR), Random Forest, and Extreme Gradient Boosting (XGBoost)), three deep learning models (Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)), and a classical time series model, Autoregressive Integrated Moving Average (ARIMA) for predicting daily energy consumption. The dataset comprises 8,040 records generated over an 11-month period from January to November 2020 from a non-refrigerated logistics facility located in Ireland. The grid search method was used to identify the best configurations for each model. The proposed XGBoost models outperform other models for both very short load forecasting (VSTLF) and short term load forecasting (STLF); the ARIMA model performed the worst.

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