Parsimonious Modelling for Estimating Hospital Cooling Demand to Improve Energy Efficiency

2021 ◽  
Author(s):  
Eduardo Dulce-Chamorro ◽  
Francisco Javier Martinez-de-Pison
Keyword(s):  
Energy Efficiency ◽  
Cooling System ◽  
Parameter Tuning ◽  
Low Complexity ◽  
Gradient Boosting ◽  
Support Vector ◽  
Improve Energy Efficiency ◽  
Hospital Facility ◽  
Parsimonious Model ◽  
Thermal Cooling

Abstract Of all the different types of public buildings, hospitals are the biggest energy consumers. Cooling systems for air conditioning and healthcare uses are particularly energy intensive. Forecasting hospital thermal-cooling demand is a remarkable and innovative method capable of improving the overall energy efficiency of an entire cooling system. Predictive models allow users to forecast the activity of water-cooled generators and adapt power generation to the real demand expected for the day ahead, while avoiding inefficient subcooling. In addition, the maintenance costs related to unnecessary starts and stops and power-generator breakdowns occurring over the long term can be reduced. This study is based on the operations of a real hospital facility and details the steps taken to develop an optimal and efficient model based on a genetic methodology that searches for low-complexity models through feature selection, parameter tuning and parsimonious model selection. The methodology, called GAparsimony, has been tested with neural networks, support vector machines and gradient boosting techniques. Finally, a weighted combination of the three best models was created. The new operational method employed herein can be replicated in similar buildings with similar water-cooled generators.

scholarly journals HyP-ABC: A Novel Automated Hyper-Parameter Tuning Algorithm Using Evolutionary Optimization

2021 ◽  
Author(s):  
Leila Zahedi ◽  
Farid Ghareh Mohammadi ◽  
M. Hadi Amini
Keyword(s):  
Parameter Optimization ◽  
Real World ◽  
Optimization Problems ◽  
State Of The Art ◽  
Parameter Tuning ◽  
Gradient Boosting ◽  
Support Vector ◽  
Wide Range ◽  
Extreme Gradient Boosting ◽  
Art Techniques

Machine learning techniques lend themselves as promising decision-making and analytic tools in a wide range of applications. Different ML algorithms have various hyper-parameters. In order to tailor an ML model towards a specific application, a large number of hyper-parameters should be tuned. Tuning the hyper-parameters directly affects the performance (accuracy and run-time). However, for large-scale search spaces, efficiently exploring the ample number of combinations of hyper-parameters is computationally challenging. Existing automated hyper-parameter tuning techniques suffer from high time complexity. In this paper, we propose HyP-ABC, an automatic innovative hybrid hyper-parameter optimization algorithm using the modified artificial bee colony approach, to measure the classification accuracy of three ML algorithms, namely random forest, extreme gradient boosting, and support vector machine. Compared to the state-of-the-art techniques, HyP-ABC is more efficient and has a limited number of parameters to be tuned, making it worthwhile for real-world hyper-parameter optimization problems. We further compare our proposed HyP-ABC algorithm with state-of-the-art techniques. In order to ensure the robustness of the proposed method, the algorithm takes a wide range of feasible hyper-parameter values, and is tested using a real-world educational dataset.

scholarly journals The Exploration of Network Coding in IEEE 802.15.4 Networks

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
Deze Zeng ◽  
Song Guo ◽  
Victor Leung ◽  
Jiankun Hu
Keyword(s):  
Energy Efficiency ◽  
Network Coding ◽  
Ieee 802.15.4 ◽  
Low Complexity ◽  
Personal Area Networks ◽  
Wireless Personal Area Networks ◽  
Home Office ◽  
Improve Energy Efficiency ◽  
Energy Constrained ◽  
Time Critical

Wireless personal area networks (WPANs) are getting popular in a variety of fields such as smart home, office automation, and e-healthcare. In WPANs, most devices are considerably energy constrained, so the communication protocol should be energy efficient. The IEEE 802.15.4 is designed as a standard protocol for low power, low data rate, low complexity, and short range connections in WPANs. The standard supports allocating several numbers of collision-free guarantee time slots (GTSs) within a superframe for some time-critical transmissions. Recently, COPE was proposed as a promising network coding architecture to essentially improve the throughput of wireless networks. In this paper, we exploit the network coding technique at coordinators to improve energy efficiency of the WPAN. Some related practical issues, such as GTS allocation and multicast, are also discussed in order to exploit the network coding opportunities efficiently. Since the coding opportunities are mostly exploited, our proposal achieves both higher energy efficiency and throughput performance than the original IEEE 802.15.4.

scholarly journals HyP-ABC: A Novel Automated Hyper-Parameter Tuning Algorithm Using Evolutionary Optimization

2021 ◽  
Author(s):  
Leila Zahedi ◽  
Farid Ghareh Mohammadi ◽  
M. Hadi Amini
Keyword(s):  
Real World ◽  
Large Scale ◽  
Convergence Rates ◽  
Parameter Tuning ◽  
Population Based ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Support Vector ◽  
Wide Range ◽  
Extreme Gradient Boosting

<p>Machine learning techniques lend themselves as promising decision-making and analytic tools in a wide range of applications. Different ML algorithms have various hyper-parameters. In order to tailor an ML model towards a specific application working at its best, its hyper-parameters should be tuned. Tuning the hyper-parameters directly affects the performance. However, for large-scale search spaces, efficiently exploring the ample number of combinations of hyper-parameters is computationally expensive. Many of the automated hyper-parameter tuning techniques suffer from low convergence rates and high experimental time complexities. In this paper, we propose HyP-ABC, an automatic innovative hybrid hyper-parameter optimization algorithm using the modified artificial bee colony approach, to measure the classification accuracy of three ML algorithms: random forest, extreme gradient boosting, and support vector machine. In order to ensure the robustness of the proposed method, the algorithm takes a wide range of feasible hyper-parameter values and is tested using a real-world educational dataset. Experimental results show that HyP-ABC is competitive with state-of-the-art techniques. Also, it has fewer hyper-parameters to be tuned than other population-based algorithms, making it worthwhile for real-world HPO problems.</p>

scholarly journals Identifying Modes of Driving Railway Trains from GPS Trajectory Data: An Ensemble Classifier-Based Approach

2018 ◽  
Vol 7 (8) ◽  
pp. 308 ◽  
Author(s):  
Han Zheng ◽  
Zanyang Cui ◽  
Xingchen Zhang
Keyword(s):  
Nearest Neighbor ◽  
Capacity Utilization ◽  
Real Data ◽  
Parameter Tuning ◽  
Integrated Approach ◽  
Ensemble Classifier ◽  
Gradient Boosting ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Trajectory Data

Recognizing Modes of Driving Railway Trains (MDRT) can help to solve railway freight transportation problems in driver behavior research, auto-driving system design and capacity utilization optimization. Previous studies have focused on analyses and applications of MDRT, but there is currently no approach to automatically and effectively identify MDRT in the context of big data. In this study, we propose an integrated approach including data preprocessing, feature extraction, classifiers modeling, training and parameter tuning, and model evaluation to infer MDRT using GPS data. The highlights of this study are as follows: First, we propose methods for extracting Driving Segmented Standard Deviation Features (DSSDF) combined with classical features for the purpose of improving identification performances. Second, we find the most suitable classifier for identifying MDRT based on a comparison of performances of K-Nearest Neighbor, Support Vector Machines, AdaBoost, Random Forest, Gradient Boosting Decision Tree, and XGBoost. From the real-data experiment, we conclude that: (i) The ensemble classifier XGBoost produces the best performance with an accuracy of 92.70%; (ii) The group of DSSDF plays an important role in identifying MDRT with an accuracy improvement of 11.2% (using XGBoost). The proposed approach has been applied in capacity utilization optimization and new driver training for the Baoshen Railway.

scholarly journals Towards Assessing the Electricity Demand in Brazil: Data-Driven Analysis and Ensemble Learning Models

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1407 ◽  
Author(s):  
João Vitor Leme ◽  
Wallace Casaca ◽  
Marilaine Colnago ◽  
Maurício Araújo Dias
Keyword(s):  
Energy Demand ◽  
Power Grid ◽  
Parameter Tuning ◽  
Gradient Boosting ◽  
Support Vector ◽  
Learning Approaches ◽  
Learning Models ◽  
Related Data ◽  
Electricity Cost ◽  
Forecasting Errors

The prediction of electricity generation is one of the most important tasks in the management of modern energy systems. Improving the assertiveness of this prediction can support government agencies, electric companies, and power suppliers in minimizing the electricity cost to the end consumer. In this study, the problem of forecasting the energy demand in the Brazilian Interconnected Power Grid was addressed, by gathering different energy-related datasets taken from public Brazilian agencies into a unified and open database, used to tune three machine learning models. In contrast to several works in the Brazilian context, which provide only annual/monthly load estimations, the learning approaches Random Forest, Gradient Boosting, and Support Vector Machines were trained and optimized as new ensemble-based predictors with parameter tuning to reach accurate daily/monthly forecasts. Moreover, a detailed and in-depth exploration of energy-related data as obtained from the Brazilian power grid is also given. As shown in the validation study, the tuned predictors were effective in producing very small forecasting errors under different evaluation scenarios.

scholarly journals Forecasting Time-Series Energy Data in Buildings Using an Additive Artificial Intelligence Model for Improving Energy Efficiency

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ngoc-Son Truong ◽  
Ngoc-Tri Ngo ◽  
Anh-Duc Pham
Keyword(s):  
Energy Efficiency ◽  
Energy Use ◽  
Predictive Accuracy ◽  
Residential Buildings ◽  
Absolute Error ◽  
Photovoltaic System ◽  
Percentage Error ◽  
Support Vector ◽  
Building Energy Efficiency ◽  
Improve Energy Efficiency

Building energy efficiency is important because buildings consume a significant energy amount. The study proposed additive artificial neural networks (AANNs) for predicting energy use in residential buildings. A dataset in hourly resolution was used to evaluate the AANNs model, which was collected from a residential building with a solar photovoltaic system. The proposed AANNs model achieved good predictive accuracy with 14.04% in mean absolute percentage error (MAPE) and 111.98 Watt-hour in the mean absolute error (MAE). Compared to the support vector regression (SVR), the AANNs model can significantly improve the accuracy which was 103.75% in MAPE. Compared to the ANNs model, accuracy improvement percentage by the AANNs model was 4.6% in MAPE. The AANNs model was the most effective forecasting model among the investigated models in predicting energy consumption, which provides building managers with a useful tool to improve energy efficiency in buildings.

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
Author(s):  
Junnan Zhao ◽  
Lu Zhu ◽  
Weineng Zhou ◽  
Lingfeng Yin ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Regression Tree ◽  
Large Data ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

scholarly journals Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1994
Author(s):  
Qian Ma ◽  
Wenting Han ◽  
Shenjin Huang ◽  
Shide Dong ◽  
Guang Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Confusion Matrix ◽  
Object Oriented ◽  
Low Complexity ◽  
Classification Model ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
Multispectral Images ◽  
High Complexity ◽  
Structure Complexity

This study explores the classification potential of a multispectral classification model for farmland with planting structures of different complexity. Unmanned aerial vehicle (UAV) remote sensing technology is used to obtain multispectral images of three study areas with low-, medium-, and high-complexity planting structures, containing three, five, and eight types of crops, respectively. The feature subsets of three study areas are selected by recursive feature elimination (RFE). Object-oriented random forest (OB-RF) and object-oriented support vector machine (OB-SVM) classification models are established for the three study areas. After training the models with the feature subsets, the classification results are evaluated using a confusion matrix. The OB-RF and OB-SVM models’ classification accuracies are 97.09% and 99.13%, respectively, for the low-complexity planting structure. The equivalent values are 92.61% and 99.08% for the medium-complexity planting structure and 88.99% and 97.21% for the high-complexity planting structure. For farmland with fragmentary plots and a high-complexity planting structure, as the planting structure complexity changed from low to high, both models’ overall accuracy levels decreased. The overall accuracy of the OB-RF model decreased by 8.1%, and that of the OB-SVM model only decreased by 1.92%. OB-SVM achieves an overall classification accuracy of 97.21%, and a single-crop extraction accuracy of at least 85.65%. Therefore, UAV multispectral remote sensing can be used for classification applications in highly complex planting structures.

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