scholarly journals Generating Synthetic Energy Usage Data to Enable Machine Learning for Sustainable Accommodation

Author(s):  
Peter J. Bentley ◽  
Soo Ling Lim ◽  
Shrey Jindal ◽  
Sid Narang
Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1200
Author(s):  
Yong-Joon Jun ◽  
Seung-ho Ahn ◽  
Kyung-Soon Park

The Green Remodeling Project under South Korea’s Green New Deal policy is a government-led project intended to strengthen the performance sector directly correlated with energy performance among various elements of improvement applicable to building remodeling by replacing insulation materials, introducing new and renewable energy, introducing high-efficiency equipment, etc., with public buildings taking the lead in green remodeling in order to induce energy efficiency enhancement in private buildings. However, there is an ongoing policy that involves the application of a fragmentary value judgment criterion, i.e., whether to apply technical elements confined to the enhancement of the energy performance of target buildings and the prediction of improvement effects according thereto, thus resulting in the phenomenon of another important value criterion for green remodeling, i.e., the enhancement of the occupant (user) comfort performance of target buildings as one of its purposes, being neglected instead. In order to accurately grasp the current status of these problems and to promote ‘expansion of the value judgment criteria for green remodeling’ as an alternative, this study collected energy usage data of buildings actually used by public institutions and then conducted a total analysis. After that, the characteristics of energy usage were analyzed for each of the groups of buildings classified by year of completion, thereby carrying out an analysis of the correlation between the non-architectural elements affecting the actual energy usage and the actual energy usage data. The correlation between the improvement performance of each technical element and the actual improvement effect was also analyzed, thereby ascertaining the relationship between the direction of major policy strategies and the actual energy usage. As a result of the relationship analysis, it was confirmed that the actual energy usage is more affected by the operating conditions of the relevant building than the application of individual strategic elements such as the performance of the envelope insulation and the performance of the high-efficiency system. In addition, it was also confirmed that the usage of public buildings does not increase in proportion to their aging. The primary goal of reducing energy usage in target buildings can be achieved if public sector (government)-led green remodeling is pushed ahead with in accordance with biased value judgment criteria, just as in the case of a campaign to refrain from operating cooling facilities in aging public buildings. However, it was possible to grasp through the progress of this study that the remodeling may also result in the deterioration of environmental comfort and stability, such as the numerical value of the indoor thermal environment. The results of this study have the significance of providing basic data for pushing ahead with a green remodeling policy in which the value judgment criteria for aging existing public buildings are more expanded, and it is necessary to continue research in such a direction that the quantitative purpose of green remodeling, which is to reduce energy usage in aging public buildings, and its qualitative purpose, which is to enhance their environmental performance for occupants’ comfort, can be mutually balanced and secured at the same time.


2015 ◽  
Vol 15 (1) ◽  
pp. 6-16 ◽  
Author(s):  
Wei Yu ◽  
Dou An ◽  
David Griffith ◽  
Qingyu Yang ◽  
Guobin Xu

2019 ◽  
Vol 16 (2) ◽  
pp. 541-564
Author(s):  
Mathias Longo ◽  
Ana Rodriguez ◽  
Cristian Mateos ◽  
Alejandro Zunino

In-silico research has grown considerably. Today?s scientific code involves long-running computer simulations and hence powerful computing infrastructures are needed. Traditionally, research in high-performance computing has focused on executing code as fast as possible, while energy has been recently recognized as another goal to consider. Yet, energy-driven research has mostly focused on the hardware and middleware layers, but few efforts target the application level, where many energy-aware optimizations are possible. We revisit a catalog of Java primitives commonly used in OO scientific programming, or micro-benchmarks, to identify energy-friendly versions of the same primitive. We then apply the micro-benchmarks to classical scientific application kernels and machine learning algorithms for both single-thread and multi-thread implementations on a server. Energy usage reductions at the micro-benchmark level are substantial, while for applications obtained reductions range from 3.90% to 99.18%.


Author(s):  
Anitha Kumari K ◽  
Indusha M ◽  
Abarna Devi D ◽  
Dheva Dharshini S

With the advancement of technology, existence of energy meters are not merely to measure energy units. The proliferation of energy meter deployments had led to significant interest in analyzing the energy usage by the machines. Energy meter data is often difficult to analyzeowing to the aggregation of many disparate and complex loads. At utility scales, analysis is further complicated by the vast quantity of data and hence industries turn towards applying machine learning techniques for monitoring and measuring loads of the machines. The energy meter data analysis aims at analyzing the behavior of the machine and providing insights on usage of the energy. This will help the industries to identify the faults in the machine and to rectify it.Two use cases with two different motor specifications is considered for evaluation and the efficiency is proved by considering accuracy, precision, F-measure and recall as metrics.


Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4918 ◽  
Author(s):  
Sanguk Park ◽  
Sangmin Park ◽  
Myeong-in Choi ◽  
Sanghoon Lee ◽  
Tacklim Lee ◽  
...  

Currently, many intelligent building energy management systems (BEMSs) are emerging for saving energy in new and existing buildings and realizing a sustainable society worldwide. However, installing an intelligent BEMS in existing buildings does not realize an innovative and advanced society because it only involves simple equipment replacement (i.e., replacement of old equipment or LED (Light Emitting Diode) lamps) and energy savings based on a stand-alone system. Therefore, artificial intelligence (AI) is applied to a BEMS to implement intelligent energy optimization based on the latest ICT (Information and Communications Technologies) technology. AI can analyze energy usage data, predict future energy requirements, and establish an appropriate energy saving policy. In this paper, we present a dynamic heating, ventilation, and air conditioning (HVAC) scheduling method that collects, analyzes, and infers energy usage data to intelligently save energy in buildings based on reinforcement learning (RL). In this regard, a hotel is used as the testbed in this study. The proposed method collects, analyzes, and infers IoT data from a building to provide an energy saving policy to realize a futuristic HVAC (heating system) system based on RL. Through this process, a purpose-oriented energy saving methodology to achieve energy saving goals is proposed.


Author(s):  
Mohd Saqib ◽  
Sanjeev Anand Sahu ◽  
Mohd Sakib ◽  
Essam A. Al-Ammar

2011 ◽  
Vol 12 (03) ◽  
pp. 241-260
Author(s):  
NIK BESSIS ◽  
NICHOLAS MCLAUCHLAN ◽  
ELEANA ASIMAKOPOULOU ◽  
ANTONY BROWN ◽  
PETER NORRINGTON

Work is underway on issues associated with the development of tools and services to reduce energy consumption. Current trends suggest that energy consumption is increasing and carbon reserves are decreasing whilst green technologies for energy generation are yet to prove themselves. In industry, there are many legacy installations of equipment capable of transmitting their energy usage via the MODBUS protocol. Here we introduce a means of logging energy usage data and transmitting it to a database. The motivation is that making energy users aware of their consumption can help assist them in taking informed action towards the reduction of wasted energy. Thus, we offer a state-of-the-art of possible networking technologies, which have led to a real-world implementation. We present requirements whilst we mathematically model the compression technique. On the development side, we use GSM/GPRS technology, embedded KJava runtime and a bespoke Java application as the framework to email the usage data to the database.


Author(s):  
J.H. Reed ◽  
A.H. Khan ◽  
R.P. Broadwater ◽  
A. Chandrasekaran

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