Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles

2021 ◽  
Vol 143 ◽  
pp. 110929
Author(s):  
Zeyu Wang ◽  
Jian Liu ◽  
Yuanxin Zhang ◽  
Hongping Yuan ◽  
Ruixue Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
Building Energy ◽  
Building Energy Efficiency ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Statistical and machine learning models for optimizing energy in parallel applications

2019 ◽  
Vol 33 (6) ◽  
pp. 1079-1097 ◽  
Author(s):  
Mark Endrei ◽  
Chao Jin ◽  
Minh Ngoc Dinh ◽  
David Abramson ◽  
Heidi Poxon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
High Performance ◽  
Large Scale ◽  
Energy Use ◽  
Parallel Applications ◽  
Learning Models ◽  
Trade Off ◽  
Time Required ◽  
Machine Learning Models

Rising power costs and constraints are driving a growing focus on the energy efficiency of high performance computing systems. The unique characteristics of a particular system and workload and their effect on performance and energy efficiency are typically difficult for application users to assess and to control. Settings for optimum performance and energy efficiency can also diverge, so we need to identify trade-off options that guide a suitable balance between energy use and performance. We present statistical and machine learning models that only require a small number of runs to make accurate Pareto-optimal trade-off predictions using parameters that users can control. We study model training and validation using several parallel kernels and more complex workloads, including Algebraic Multigrid (AMG), Large-scale Atomic Molecular Massively Parallel Simulator, and Livermore Unstructured Lagrangian Explicit Shock Hydrodynamics. We demonstrate that we can train the models using as few as 12 runs, with prediction error of less than 10%. Our AMG results identify trade-off options that provide up to 45% improvement in energy efficiency for around 10% performance loss. We reduce the sample measurement time required for AMG by 90%, from 13 h to 74 min.

scholarly journals Tuning machine learning models for prediction of building energy loads

2019 ◽  
Vol 47 ◽  
pp. 101484 ◽  
Author(s):  
Saleh Seyedzadeh ◽  
Farzad Pour Rahimian ◽  
Parag Rastogi ◽  
Ivan Glesk
Keyword(s):  
Machine Learning ◽  
Building Energy ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4745 ◽  
Author(s):  
Dana-Mihaela Petroșanu ◽  
George Căruțașu ◽  
Nicoleta Luminița Căruțașu ◽  
Alexandru Pîrjan
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
Scientific Literature ◽  
The Body ◽  
Learning Models ◽  
Smart Buildings ◽  
Building Management ◽  
Sensor Devices ◽  
Recent Developments ◽  
Machine Learning Models

Lately, many scientists have focused their research on subjects like smart buildings, sensor devices, virtual sensing, buildings management, Internet of Things (IoT), artificial intelligence in the smart buildings sector, improving life quality within smart homes, assessing the occupancy status information, detecting human behavior with a view to assisted living, maintaining environmental health, and preserving natural resources. The main purpose of our review consists of surveying the current state of the art regarding the recent developments in integrating supervised and unsupervised machine learning models with sensor devices in the smart building sector with a view to attaining enhanced sensing, energy efficiency and optimal building management. We have devised the research methodology with a view to identifying, filtering, categorizing, and analyzing the most important and relevant scientific articles regarding the targeted topic. To this end, we have used reliable sources of scientific information, namely the Elsevier Scopus and the Clarivate Analytics Web of Science international databases, in order to assess the interest regarding the above-mentioned topic within the scientific literature. After processing the obtained papers, we finally obtained, on the basis of our devised methodology, a reliable, eloquent and representative pool of 146 papers scientific works that would be useful for developing our survey. Our approach provides a useful up-to-date overview for researchers from different fields, which can be helpful when submitting project proposals or when studying complex topics such those reviewed in this paper. Meanwhile, the current study offers scientists the possibility of identifying future research directions that have not yet been addressed in the scientific literature or improving the existing approaches based on the body of knowledge. Moreover, the conducted review creates the premises for identifying in the scientific literature the main purposes for integrating Machine Learning techniques with sensing devices in smart environments, as well as purposes that have not been investigated yet.

scholarly journals Building Energy Information: Demand and Consumption Prediction with Machine Learning Models for Sustainable and Smart Cities

2019 ◽  
Author(s):  
Sina Faizollahzadeh ardabili ◽  
Amir Mosavi ◽  
Annamária R. Várkonyi-Kóczy
Keyword(s):  
Machine Learning ◽  
Energy Demand ◽  
Smart Cities ◽  
Building Energy ◽  
Learning Models ◽  
Information Demand ◽  
Building Energy Systems ◽  
Consumption Prediction ◽  
Energy Information ◽  
Machine Learning Models

Building energy consumption plays an essential role in urban sustainability. The prediction of the energy demand is also of particular importance for developing smart cities and urban planning. Machine learning has recently contributed to the advancement of methods and technologies to predict demand and consumption for building energy systems. This paper presents a state of the art of machine learning models and evaluates the performance of these models. Through a systematic review and a comprehensive taxonomy, the advances of machine learning are carefully investigated and promising models are introduced.

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