scholarly journals Development of Regression Models considering Time-Lag and Aerosols for Predicting Heating Loads in Buildings

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Hong Soo Lim ◽  
Gon Kim
Keyword(s):  
Energy Consumption ◽  
Prediction Models ◽  
Meteorological Data ◽  
Time Lag ◽  
Building Energy ◽  
Weather Data ◽  
Thermal Loads ◽  
Building Energy Consumption ◽  
Automation Systems ◽  
Heating Loads

Building automation systems is becoming more vital, especially in regard to reduced building energy consumption. However, the accuracy of such systems in calculating building thermal loads is limited as they are unable to predict future thermal loads based on prevailing environmental factors. The current paper therefore seeks to improve the understanding of the interactions between outdoor meteorological data and building energy consumption through a statistical analysis. Using weather data collected by the Korean Meteorological Agency (KMA) over a period of three years (2011–2014), prediction models that are able to predict heating thermal loads considering the time-lag phenomenon are developed. In addition, the study develops different prediction models for buildings of different sizes. The results confirm the existence of the time-lag phenomenon: the heating load experienced by a building at a given time is better explained by a regression model developed using the climatic conditions that existed two hours before. As such, conventional building simulation programs must endeavor to include time-lag as well as Aerosol Optical Depth (AOD) data as important factors in the prediction of building heating loads.

scholarly journals AN INVESTIGATION ON RELATIONSHIP BETWEEN ENERGY CONSUMPTION OF HIGH RISE INSTITUTIONAL BUILDINGS AND THE CLIMATE OF DHAKA CITY

2012 ◽  
Vol 12 (1) ◽  
pp. 39-48
Author(s):  
Md. Yousuf Reja ◽  
Amreen Shajahan
Keyword(s):  
Energy Consumption ◽  
Large Scale ◽  
Energy Use ◽  
Meteorological Data ◽  
Building Design ◽  
Building Energy ◽  
Tropical Region ◽  
Dhaka City ◽  
Building Energy Consumption ◽  
Public Buildings

Growth in population, mounting demand for building services and comfort levels, along with the rise in time spent inside buildings, assure the upward trend in energy consumption of large scale public buildings in Dhaka city. For this reason, energy efficiency in buildings is a prime objective today for energy policy at regional, national and international levels. This paper devotes to discuss the holistic utility bills analysis method for investigating and analyzing whole building energy consumption of public buildings with special emphasis on private sector institutions in a tropical region like Dhaka city. Correlations between operational records of energy consumption of three institutional buildings and the meteorological data including monthly mean outdoor dry-bulb temperature (To), and relative humidity (RH) of Dhaka city have been derived. The findings of the study reveals that the overall building energy consumption is highly dependent on climate, building design characteristics including internal layout, orientation, fenestration and site configurations, and ownership. The analysis of such kind of model is especially useful for building managers and owners to track energy use during preretrofit and post-retrofit periods and to reduce building operational costs in the tropical region. Keywords: Energy consumption, Institutional buildings, Utility bills, Heat gain, Meteorological data.

Cloud-edge collaboration based transferring prediction of building energy consumption

2021 ◽  
pp. 1-13
Author(s):  
Jinping Zhang ◽  
Xiaoping Deng ◽  
Chengdong Li ◽  
Guanqun Su ◽  
Yulong Yu
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Prediction Models ◽  
Historical Data ◽  
Computational Cost ◽  
Building Energy ◽  
Fine Tuning ◽  
Observation Data ◽  
Building Energy Consumption ◽  
Model Library

Building energy consumption (BEC) prediction often requires constructing a corresponding model for each building based historical data. However, the constructed model for one building is difficult to be reused in other buildings. Recent approaches have shown that cloud-edge collaboration architecture is promising in realizing model reuse. How to complete the reuse of cloud energy consumption prediction models at the edge and reduce the computational cost of the model training is one of the key issues that need to be solved. To handle the above problems, a cloud-edge collaboration based transferring prediction method for BEC is proposed in this paper. Specifically, a model library stored prediction models for different types of buildings is constructed based the historical energy consumption data and the long short-term memory (LSTM) network in the cloud firstly; then, the similarity measurement strategies of time series with different granularity are given, and the model to be transferred from the model library is matched by analyzing the similarity between observation data uploaded to the cloud and the historical data collected in the cloud; finally, the fine-tuning strategy of the matching prediction model is given, and this model is fine-tuned at the edge to achieve its reuse in concrete application scenarios. Experiments on practical datasets reveal that compared with the prediction model which doesn’t utilize the transfer strategy, the proposed prediction model has better performance according to MAE and RMSE. Experimental results also confirm that the proposed method effectively reduces the computational cost of the network training at the edge.

scholarly journals Comparing micro-scale weather data to building energy consumption in Singapore

2017 ◽  
Vol 152 ◽  
pp. 776-791 ◽  
Author(s):  
Yuezhong Liu ◽  
Rudi Stouffs ◽  
Abel Tablada ◽  
Nyuk Hien Wong ◽  
Ji Zhang
Keyword(s):  
Energy Consumption ◽  
Building Energy ◽  
Weather Data ◽  
Building Energy Consumption ◽  
Micro Scale

scholarly journals Review on Urban Heat Island in China: Methods, Its Impact on Buildings Energy Demand and Mitigation Strategies

2021 ◽  
Vol 13 (2) ◽  
pp. 762
Author(s):  
Liu Tian ◽  
Yongcai Li ◽  
Jun Lu ◽  
Jue Wang
Keyword(s):  
Energy Consumption ◽  
Urban Heat Island ◽  
Building Energy ◽  
Design Stage ◽  
Heat Island ◽  
Building Energy Consumption ◽  
Rapid Urbanization ◽  
Substantial Impact ◽  
Urban Heat ◽  
The Impact

High population density, dense high-rise buildings, and impervious pavements increase the vulnerability of cities, which aggravate the urban climate environment characterized by the urban heat island (UHI) effect. Cities in China provide unique information on the UHI phenomenon because they have experienced rapid urbanization and dramatic economic development, which have had a great influence on the climate in recent decades. This paper provides a review of recent research on the methods and impacts of UHI on building energy consumption, and the practical techniques that can be used to mitigate the adverse effects of UHI in China. The impact of UHI on building energy consumption depends largely on the local microclimate, the urban area features where the building is located, and the type and characteristics of the building. In the urban areas dominated by air conditioning, UHI could result in an approximately 10–16% increase in cooling energy consumption. Besides, the potential negative effects of UHI can be prevented from China in many ways, such as urban greening, cool material, water bodies, urban ventilation, etc. These strategies could have a substantial impact on the overall urban thermal environment if they can be used in the project design stage of urban planning and implemented on a large scale. Therefore, this study is useful to deepen the understanding of the physical mechanisms of UHI and provide practical approaches to fight the UHI for the urban planners, public health officials, and city decision-makers in China.

scholarly journals Utilising Open Geospatial Data to Refine Weather Variables for Building Energy Performance Evaluation—Incident Solar Radiation and Wind-Driven Infiltration Modelling

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 802
Author(s):  
Kristian Skeie ◽  
Arild Gustavsen
Keyword(s):  
Solar Radiation ◽  
Web Service ◽  
Meteorological Data ◽  
Surface Wind ◽  
Building Energy ◽  
Energy Performance ◽  
Geospatial Data ◽  
Weather Data ◽  
Building Site ◽  
Incident Solar Radiation

In building thermal energy characterisation, the relevance of proper modelling of the effects caused by solar radiation, temperature and wind is seen as a critical factor. Open geospatial datasets are growing in diversity, easing access to meteorological data and other relevant information that can be used for building energy modelling. However, the application of geospatial techniques combining multiple open datasets is not yet common in the often scripted workflows of data-driven building thermal performance characterisation. We present a method for processing time-series from climate reanalysis and satellite-derived solar irradiance services, by implementing land-use, and elevation raster maps served in an elevation profile web-service. The article describes a methodology to: (1) adapt gridded weather data to four case-building sites in Europe; (2) calculate the incident solar radiation on the building facades; (3) estimate wind and temperature-dependent infiltration using a single-zone infiltration model and (4) including separating and evaluating the sheltering effect of buildings and trees in the vicinity, based on building footprints. Calculations of solar radiation, surface wind and air infiltration potential are done using validated models published in the scientific literature. We found that using scripting tools to automate geoprocessing tasks is widespread, and implementing such techniques in conjunction with an elevation profile web service made it possible to utilise information from open geospatial data surrounding a building site effectively. We expect that the modelling approach could be further improved, including diffuse-shading methods and evaluating other wind shelter methods for urban settings.

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