A Non-intrusive Data-driven model for detailed Occupants’ activities classification in residential buildings using environmental and energy usage data

2021 ◽  
pp. 111699
Author(s):  
Young Ran Yoon ◽  
Ye Rin Lee ◽  
Sun Ho Kim ◽  
Jeong Won Kim ◽  
Hyeun Jun Moon
Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 271
Author(s):  
Yusung Lee ◽  
Woohyun Kim

In this study, an optimal control strategy for the variable refrigerant flow (VRF) system is developed using a data-driven model and on-site data to save the building energy. Three data-based models are developed to improve the on-site applicability. The presented models are used to determine the length of time required to bring each zone from its current temperature to the set point. The existing data are used to evaluate and validated the predictive performance of three data-based models. Experiments are conducted using three outdoor units and eight indoor units on site. The experimental test is performed to validate the performance of proposed optimal control by comparing between conventional and optimal control methods. Then, the ability to save energy wasted for maintaining temperature after temperature reaches the set points is evaluated through the comparison of energy usage. Given these results, 30.5% of energy is saved on average for each outdoor unit and the proposed optimal control strategy makes the zones comfortable.


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.


Energy ◽  
2021 ◽  
Vol 218 ◽  
pp. 119539
Author(s):  
Karthik Panchabikesan ◽  
Fariborz Haghighat ◽  
Mohamed El Mankibi

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5780
Author(s):  
Apostolos C. Tsolakis ◽  
Ilias Kalamaras ◽  
Thanasis Vafeiadis ◽  
Lampros Zyglakis ◽  
Angelina D. Bintoudi ◽  
...  

On becoming a commodity, Microgrids (MGs) have started gaining ground in various sizes (e.g., nanogrids, homegrids, etc.) and forms (e.g., local energy communities) leading an exponential growth in the respective sector. From demanding deployments such as military bases and hospitals, to tertiary and residential buildings and neighborhoods, MG systems exploit renewable and conventional generation assets, combined with various storage capabilities to deliver a completely new set of business opportunities and services in the context of the Smart Grid. As such systems involve economic, environmental and technical aspects, their performance is quite difficult to evaluate, since there are not any standards that cover all of these aspects, especially during operational stages. Towards allowing an holistic definition of a MG performance, for both design and operational stages, this paper first introduces a complete set of Key Performance Indicators to measure holistically the performance of a MG’s life cycle. Following, focusing on the MG’s day-to-day operation, a data-driven assessment is proposed, based on dynamic metrics, custom made reference models, and smart meter data, in order to be able to extract its operational performance. Two different algorithmic implementations (i.e., Dynamic Time Warping and t-distributed Stochastic Neighbor Embedding) are used to support the methodology proposed, while real-life data are used from a small scale MG to provide the desired proof-of-concept. Both algorithms seem to correctly identify days and periods of not optimal operation, hence presenting promising results for MG performance assessment, that could lead to a MG Performance Classification scheme.


2017 ◽  
Vol 7 (2) ◽  
pp. 185-198 ◽  
Author(s):  
Kamalesh Panthi ◽  
Kanchan Das ◽  
Tarek Abdel-Salam

Purpose Vacation rental homes, in general, have different energy usage characteristics than traditional residential homes mainly because of the occupancy pattern that changes on a weekly basis. These homes, predominantly larger in size, offer a greater scope for energy savings also because of the wasteful habits of their seasonal occupants. The purpose of this paper is to investigate the causes of energy inefficiencies prevalent in these homes so that appropriate retrofit choices can be offered to homeowners. Design/methodology/approach This research presents a case study of a vacation rental home whose energy consumption was investigated in depth and energy inefficiencies identified through modeling using energy modeling software, eQUEST. Simulations were performed to identify viable retrofit scenarios. Findings While improvement in the building envelope such as providing shades/overhangs on the windows, reducing infiltration and increasing insulation of the exterior wall did not show promising results for savings on energy cost, other improvements such as use of highly efficient lamps, tank-less water heater system and occupancy sensors showed viable investment options with shorter payback periods. It was also found that energy use intensity of sampled houses was about half of the average of US residential buildings, which could primarily be attributed to the seasonal nature of occupancy of these houses. Originality/value There is a dearth of literature pertaining to energy efficiency-related retrofits of coastal vacation homes. This research fills that gap to some extent by addressing this issue with an ultimate aim of assisting homeowners in retrofit decision-making.


Author(s):  
O. E. Taylor ◽  
P. S. Ezekiel ◽  
V. T. Emma

Building area is a vital consumer of all globally produced energy. Structures of buildings absorb about 40 % of the total energy created which transcription about 30 % of the integral worldwide CO2 radiations. As such, reducing the measure of energy absorbed by the building area would incredibly help the much-crucial depletions in world energy utilization and the related ecological concerns. This paper presents a smart system for thermal comfort prediction on residential buildings using data driven model with Random Forest Classifier. The system starts by acquiring a global thermal comfort data, pre-processed the acquired data, by removing missing values and duplicated values, and also reduced the numbers of features in the dataset by selecting just twelve columns out of 70 columns in total. This process is called feature extraction. After the pre-processing and feature extraction, the dataset was split into a training and testing set. The training set was 70% while the testing set was 30% of the original dataset. The training data was used in training our thermal comfort model with Random Forest Classifier. After training, Random Forest Classifier had an accuracy of 99.99% which is about 100% approximately. We then save our model and deployed to web through python flask, so that users can use it in predicting real time thermal comfort in their various residential buildings.


2022 ◽  
Author(s):  
Maurice Meyer ◽  
Ingrid Wiederkehr ◽  
Melina Panzner ◽  
Christian Koldewey ◽  
Roman Dumitrescu

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