scholarly journals Correlating energy consumption with multi-unit residential building characteristics in the city of Toronto

2013 ◽  
Vol 66 ◽  
pp. 648-656 ◽  
Author(s):  
Marianne F. Touchie ◽  
Clarissa Binkley ◽  
Kim D. Pressnail
Keyword(s):  
Energy Consumption ◽  
Residential Building ◽  
Building Characteristics ◽  
The City

Simulation of Residential Energy Consumption Based on Software of DeST

2013 ◽  
Vol 448-453 ◽  
pp. 1269-1272
Author(s):  
Zhao Chen ◽  
Li Bai ◽  
Feng Li
Keyword(s):  
Energy Consumption ◽  
System Design ◽  
Energy Saving ◽  
Residential Building ◽  
Heating System ◽  
Residential Energy ◽  
Residential Energy Consumption ◽  
Heating Energy Consumption ◽  
Uneven Heating ◽  
The City

In this paper, the software of DeST was used to simulate the heating energy consumption by the year of a typical energy-saving residential building in the city of Changchun. Comparing the energy consumption of the top and bottom,the middle room and the edges rooms ,we get the reasons for the uneven heating and put forward the corresponding solutions, which provide the reference for heating system design.

ANALISIS PENILAIAN BANGUNAN RUMAH TINGGAL DI KOTA DENPASAR

1970 ◽  
Author(s):  
M. Mariada Rijasa ◽  
M. Sukrawa ◽  
Mayun Nadiasa
Keyword(s):  
Literature Review ◽  
Negative Impact ◽  
Residential Buildings ◽  
Residential Building ◽  
Dominant Factor ◽  
Validity And Reliability ◽  
Building Area ◽  
Road Width ◽  
Building Characteristics ◽  
The City

Research on factors that affect the value of residential buildings in the city of Denpasar has been done consisting of literature review, interviews with experts, data collection and statistical analysis. Obtained from literature review were 45 factors which then grouped into four, namely: land characteristics, environment, location, and building characteristics. Survey on 27 valuation expert respondents was done to obtain their perceptions on the factors, and then their perceptions were measured with Likert scale. The data were then statistically tested to determine its validity and reliability, after which factor analysis was performed to obtain factors that truly valid within its group. To further evaluate the dominant factor in each group, two hundred data of previously assessed residential buildings were collected and analyzed using multiple linear regression. Results showed that group of factors that affect the value of residential building the most is location (7.723) followed by environment (3.843), building characteristics (3,741) and land characteristics (3.253). Downtown area, road width, building area, and land area are the factor of location, environment, building characteristics, and land characteristics, respectively, that dominantly showed positive effect within its group. SUTET transmission, poor road conditions, poor physical condition of the house, and the land at road end "tusuk sate" dominantly showed negative impact within its group.

scholarly journals Energy Analyses of Serbian Buildings with Horizontal Overhangs: A Case Study

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4577 ◽  
Author(s):  
Danijela Nikolic ◽  
Slobodan Djordjevic ◽  
Jasmina Skerlic ◽  
Jasna Radulovic
Keyword(s):  
Energy Consumption ◽  
Residential Building ◽  
Primary Energy ◽  
Embodied Energy ◽  
Building Energy Efficiency ◽  
Total Energy Consumption ◽  
Software Environment ◽  
Annual Consumption ◽  
The City

It is well known that nowadays a significant part of the total energy consumption is related to buildings, so research for improving building energy efficiency is very important. This paper presents our investigations about the dimensioning of horizontal overhangs in order to determine the minimum annual consumption of building primary energy for heating, cooling and lighting. In this investigation, embodied energy for horizontal roof overhangs was taken into account. The annual simulation was carried out for a residential building located in the city of Belgrade (Serbia). Horizontal overhangs (roof and balcony) are positioned to provide shading of all exterior of the building. The building is simulated in the EnergyPlus software environment. The optimization of the overhang size was performed by using the Hooke Jeeves algorithm and plug-in GenOpt program. The objective function minimizes the annual consumption of primary energy for heating, cooling and lighting of the building and energy spent to build overhangs. The simulation results show that the building with optimally sized roof and balcony overhangs consumed 7.12% lessprimary energy for heating, cooling and lighting, compared to the house without overhangs. A 44.15% reduction in cooling energy consumption is also achieved.

scholarly journals Computation offloading through mobile vehicles in IoT-edge-cloud network

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Jun Long ◽  
Yueyi Luo ◽  
Xiaoyu Zhu ◽  
Entao Luo ◽  
Mingfeng Huang
Keyword(s):  
Reinforcement Learning ◽  
Energy Consumption ◽  
Low Cost ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Challenging Problem ◽  
Learning Technique ◽  
Cloud Network ◽  
The City ◽  
Task Offloading

AbstractWith the developing of Internet of Things (IoT) and mobile edge computing (MEC), more and more sensing devices are widely deployed in the smart city. These sensing devices generate various kinds of tasks, which need to be sent to cloud to process. Usually, the sensing devices do not equip with wireless modules, because it is neither economical nor energy saving. Thus, it is a challenging problem to find a way to offload tasks for sensing devices. However, many vehicles are moving around the city, which can communicate with sensing devices in an effective and low-cost way. In this paper, we propose a computation offloading scheme through mobile vehicles in IoT-edge-cloud network. The sensing devices generate tasks and transmit the tasks to vehicles, then the vehicles decide to compute the tasks in the local vehicle, MEC server or cloud center. The computation offloading decision is made based on the utility function of the energy consumption and transmission delay, and the deep reinforcement learning technique is adopted to make decisions. Our proposed method can make full use of the existing infrastructures to implement the task offloading of sensing devices, the experimental results show that our proposed solution can achieve the maximum reward and decrease delay.

scholarly journals Identification of Cost-Optimal Measures for Energy Renovation of Thermal Envelopes in Different Types of Public School Buildings in the City of Valencia

2021 ◽  
Vol 11 (11) ◽  
pp. 5108
Author(s):  
María Esther Liébana-Durán ◽  
Begoña Serrano-Lanzarote ◽  
Leticia Ortega-Madrigal
Keyword(s):  
Public Schools ◽  
Energy Consumption ◽  
Public Administration ◽  
School Buildings ◽  
Regulatory Requirements ◽  
Building Stock ◽  
Eu Directive ◽  
Global Cost ◽  
The City ◽  
The Eu

In order to achieve the EU emission reduction goals, it is essential to renovate the building stock, by improving energy efficiency and promoting total decarbonisation. According to the 2018/844/EU Directive, 3% of Public Administration buildings should be renovated every year. So as to identify the measures to be applied in those buildings and obtain the greatest reduction in energy consumption at the lowest cost, the Directive 2010/31/EU proposed a cost-optimisation-based methodology. The implementation of this allowed to carry out studies in detail in actual scenarios for the energy renovation of thermal envelopes of public schools in the city of Valencia. First, primary school buildings were analysed and classified into three representative types. For each type, 21 sets of measures for improving building thermal envelopes were proposed, considering the global cost, in order to learn about the savings obtained, the repayment term for the investment made, the percentage reduction in energy consumption and the level of compliance with regulatory requirements. The result and conclusions will help Public Administration in Valencia to draw up an energy renovation plan for public building schools in the city.

scholarly journals Ensemble Prediction Approach Based on Learning to Statistical Model for Efficient Building Energy Consumption Management

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 405
Author(s):  
Anam Nawaz Khan ◽  
Naeem Iqbal ◽  
Rashid Ahmad ◽  
Do-Hyeun Kim
Keyword(s):  
Energy Consumption ◽  
Statistical Model ◽  
Residential Buildings ◽  
Residential Building ◽  
Experimental Results ◽  
Consumption Patterns ◽  
Ensemble Prediction ◽  
Short Term ◽  
Term Energy ◽  
Prediction Approach

With the development of modern power systems (smart grid), energy consumption prediction becomes an essential aspect of resource planning and operations. In the last few decades, industrial and commercial buildings have thoroughly been investigated for consumption patterns. However, due to the unavailability of data, the residential buildings could not get much attention. During the last few years, many solutions have been devised for predicting electric consumption; however, it remains a challenging task due to the dynamic nature of residential consumption patterns. Therefore, a more robust solution is required to improve the model performance and achieve a better prediction accuracy. This paper presents an ensemble approach based on learning to a statistical model to predict the short-term energy consumption of a multifamily residential building. Our proposed approach utilizes Long Short-Term Memory (LSTM) and Kalman Filter (KF) to build an ensemble prediction model to predict short term energy demands of multifamily residential buildings. The proposed approach uses real energy data acquired from the multifamily residential building, South Korea. Different statistical measures are used, such as mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and R2 score, to evaluate the performance of the proposed approach and compare it with existing models. The experimental results reveal that the proposed approach predicts accurately and outperforms the existing models. Furthermore, a comparative analysis is performed to evaluate and compare the proposed model with conventional machine learning models. The experimental results show the effectiveness and significance of the proposed approach compared to existing energy prediction models. The proposed approach will support energy management to effectively plan and manage the energy supply and demands of multifamily residential buildings.

scholarly journals Towards Sustainable Energy Retrofitting, a Simulation for Potential Energy Use Reduction in Residential Buildings in Palestine

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3876
Author(s):  
Sameh Monna ◽  
Adel Juaidi ◽  
Ramez Abdallah ◽  
Aiman Albatayneh ◽  
Patrick Dutournie ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Energy Savings ◽  
Residential Buildings ◽  
Residential Building ◽  
Simulation Models ◽  
Water Heating ◽  
Heating And Cooling ◽  
Space Cooling ◽  
Cooling Loads

Since buildings are one of the major contributors to global warming, efforts should be intensified to make them more energy-efficient, particularly existing buildings. This research intends to analyze the energy savings from a suggested retrofitting program using energy simulation for typical existing residential buildings. For the assessment of the energy retrofitting program using computer simulation, the most commonly utilized residential building types were selected. The energy consumption of those selected residential buildings was assessed, and a baseline for evaluating energy retrofitting was established. Three levels of retrofitting programs were implemented. These levels were ordered by cost, with the first level being the least costly and the third level is the most expensive. The simulation models were created for two different types of buildings in three different climatic zones in Palestine. The findings suggest that water heating, space heating, space cooling, and electric lighting are the highest energy consumers in ordinary houses. Level one measures resulted in a 19–24 percent decrease in energy consumption due to reduced heating and cooling loads. The use of a combination of levels one and two resulted in a decrease of energy consumption for heating, cooling, and lighting by 50–57%. The use of the three levels resulted in a decrease of 71–80% in total energy usage for heating, cooling, lighting, water heating, and air conditioning.

scholarly journals The Impact Assessment of Climate Change on Building Energy Consumption in Poland

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4084
Author(s):  
Hassan Bazazzadeh ◽  
Peiman Pilechiha ◽  
Adam Nadolny ◽  
Mohammadjavad Mahdavinejad ◽  
Seyedeh sara Hashemi safaei
Keyword(s):  
Climate Change ◽  
Energy Consumption ◽  
Thermal Load ◽  
Building Energy ◽  
Building Energy Consumption ◽  
Building Sector ◽  
Energy Demands ◽  
Heating Load ◽  
The Impact ◽  
The City

A substantial share of the building sector in global energy demand has attracted scholars to focus on the energy efficiency of the building sector. The building’s energy consumption has been projected to increase due to mass urbanization, high living comfort standards, and, more importantly, climate change. While climate change has potential impacts on the rate of energy consumption in buildings, several studies have shown that these impacts differ from one region to another. In response, this paper aimed to investigate the impact of climate change on the heating and cooling energy demands of buildings as influential variables in building energy consumption in the city of Poznan, Poland. In this sense, through the statistical downscaling method and considering the most recent Typical Meteorological Year (2004–2018) as the baseline, the future weather data for 2050 and 2080 of the city of Poznan were produced according to the HadCM3 and A2 GHG scenario. These generated files were then used to simulate the energy demands in 16 building prototypes of the ASHRAE 90.1 standard. The results indicate an average increase in cooling load and a decrease in heating load at 135% and 40% , respectively, by 2080. Due to the higher share of heating load, the total thermal load of the buildings decreased within the study period. Therefore, while the total thermal load is currently under the decrease, to avoid its rise in the future, serious measures should be taken to control the increased cooling demand and, consequently, thermal load and GHG emissions.

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