scholarly journals Estimation of load duration curves from general building data in the building stock using dynamic BES-models

2019 ◽  
Vol 111 ◽  
pp. 01078 ◽  
Author(s):  
Rana M. Mahmoud ◽  
Mohsen Sharifi ◽  
Eline Himpe ◽  
Marc Delghust ◽  
Jelle Laverge
Keyword(s):  
Cooling System ◽  
Physical Parameters ◽  
Cooling Load ◽  
Building Stock ◽  
Load Duration ◽  
Heating And Cooling ◽  
Building Information ◽  
Geometrical Data ◽  
Cooling Loads ◽  
Heating And Cooling Load

Modelling and simulation of building stock is a valuable source of information for investigating the feasibility of implementing new heating and cooling system technologies. Some of these technologies have oversizing problem as the designers rely on their experience and previous knowledge. Building stock modelling can provide a solution for more accurate designing process. However, some of the current building stock modelling methods uses a representative building which can exclude whole ranges of the different combinations of building geometry and physical properties that can be crucial for heating and cooling load estimation. Therefore, we developed a methodology that allows faster and accurate building energy simulation (BES) multizone models from general building information of the whole building stock that is able to estimate load duration. This will help engineers and designers to decide on the system sizing at the early design stages. This paper presents first, the process of generating dynamically heating and cooling load duration curves by using BES-models from general geometrical data of the building stock. Second, we examine the process on a sample of the building stock where geometrical and physical parameters were varied. The workflow of the process has worked successfully, generating heating and cooling duration curves for 14 case studies. We observed that heating and cooling loads are highly influenced by different combinations of parameters. High glazing percentage affects highly the heat losses, thus more heating loads. Besides, for a west oriented building, the high glazing percentage combined with high internal gains can be the reason for significant cooling loads. In next steps, we are going to extend the current methodology to cover different building typologies within different climates across Europe.

scholarly journals Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings

2020 ◽  
Vol 10 (11) ◽  
pp. 3829 ◽  
Author(s):  
Arash Moradzadeh ◽  
Amin Mansour-Saatloo ◽  
Behnam Mohammadi-Ivatloo ◽  
Amjad Anvari-Moghaddam
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Residential Buildings ◽  
Load Forecasting ◽  
Training Data ◽  
Support Vector ◽  
Cooling Load ◽  
Heating And Cooling ◽  
Cooling Loads ◽  
Heating And Cooling Load

Nowadays, since energy management of buildings contributes to the operation cost, many efforts are made to optimize the energy consumption of buildings. In addition, the most consumed energy in the buildings is assigned to the indoor heating and cooling comforts. In this regard, this paper proposes a heating and cooling load forecasting methodology, which by taking this methodology into the account energy consumption of the buildings can be optimized. Multilayer perceptron (MLP) and support vector regression (SVR) for the heating and cooling load forecasting of residential buildings are employed. MLP and SVR are the applications of artificial neural networks and machine learning, respectively. These methods commonly are used for modeling and regression and produce a linear mapping between input and output variables. Proposed methods are taught using training data pertaining to the characteristics of each sample in the dataset. To apply the proposed methods, a simulated dataset will be used, in which the technical parameters of the building are used as input variables and heating and cooling loads are selected as output variables for each network. Finally, the simulation and numerical results illustrates the effectiveness of the proposed methodologies.

Analysis of the Indoor Thermal Load Variations Depending on the Shading Device Parameters and Window Orientation

2015 ◽  
Vol 23 (03) ◽  
pp. 1550023 ◽  
Author(s):  
Yeo Beom Yoon ◽  
Rashmi Manandhar ◽  
Kwang Ho Lee
Keyword(s):  
Vertical Wall ◽  
Building Performance ◽  
Cooling Load ◽  
Load Variations ◽  
Heating And Cooling ◽  
Window System ◽  
Shading Devices ◽  
Shading Device ◽  
Cooling Loads ◽  
Heating And Cooling Load

Many studies have been done to study the advantage of using window shading devices as a means of controlling solar penetration into the building. Shading devices like blinds have been proved to have a significant effect on the heating and cooling load of the building. As it is easier and less costly to change blinds than changing the window system in a building, using blinds is a very effective way of improving building performance. Although many studies have been done, mostly the study focuses on window that is oriented towards the south. As it is obvious that in a real building windows can be facing any direction, in this study the effect of blinds on heating and cooling loads of a building has been done, when the design of blind is either horizontal or vertical, when it is placed either inside or outside and when the slat angle automatically changes based on either solar energy received on vertical wall or on horizontal surface (roof).

A STUDY ON THE APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES FOR PREDICTING THE HEATING AND COOLING LOADS OF BUILDINGS

2019 ◽  
Vol 14 (3) ◽  
pp. 115-128 ◽  
Author(s):  
Sushmita Das ◽  
Aleena Swetapadma ◽  
Chinmoy Panigrahi
Keyword(s):  
Artificial Intelligence ◽  
Energy Efficient ◽  
Energy Performance ◽  
Accurate Estimation ◽  
Cooling Load ◽  
Alternative Technologies ◽  
Heating And Cooling ◽  
Energy Efficient Buildings ◽  
Cooling Loads ◽  
Heating And Cooling Load

The prediction of the heating and cooling loads of a building is an essential aspect in studies involving the analysis of energy consumption in buildings. An accurate estimation of heating and cooling load leads to better management of energy related tasks and progressing towards an energy efficient building. With increasing global energy demands and buildings being major energy consuming entities, there is renewed interest in studying the energy performance of buildings. Alternative technologies like Artificial Intelligence (AI) techniques are being widely used in energy studies involving buildings. This paper presents a review of research in the area of forecasting the heating and cooling load of buildings using AI techniques. The results discussed in this paper demonstrate the use of AI techniques in the estimation of the thermal loads of buildings. An accurate prediction of the heating and cooling loads of buildings is necessary for forecasting the energy expenditure in buildings. It can also help in the design and construction of energy efficient buildings.

Impact of building design parameters precision on heating and cooling load calculations

2018 ◽  
Vol 38 (2) ◽  
pp. 741-749
Author(s):  
Sajad Abasnezhad ◽  
Nima Soltani ◽  
Elin Markarian ◽  
Hamed Aghabalayi Fakhim ◽  
Hamed Khezerloo
Keyword(s):  
Building Design ◽  
Design Parameters ◽  
Cooling Load ◽  
Heating And Cooling ◽  
Heating And Cooling Load

The Effects of Window Film on the Performance of Window System

2014 ◽  
Vol 525 ◽  
pp. 408-411
Author(s):  
Min Seon Jang ◽  
Gyeong Seok Choi ◽  
Jae Sik Kang ◽  
Yumin Kim
Keyword(s):  
Energy Consumption ◽  
Cooling Load ◽  
Indoor Temperature ◽  
Heating And Cooling ◽  
Window System ◽  
Window Systems ◽  
Clear Glass ◽  
Window Film ◽  
And Performance ◽  
Heating And Cooling Load

Window film is generally attached the glazing in buildings to improve the thermal performance of the window system by addressing a range of problems such as indoor temperature rise, indoor temperature imbalance, degraded heating and cooling load due to excessive influx of solar radiation. To evaluate the performance of window films, window films are attached to 3mm or 6mm clear glass. However, window films are generally used on existing window systems for reducing the annual energy consumption. Therefore it is necessary to evaluate the performance of window films depending on the performance of glazing such as clear double glazing or low-e double glazing. Thus the purpose of this study is to analyze the performance of window systems when window film is attached. As a result, in the case of applying window films for reducing the SHGC of buildings, it is necessary to select window films suitable for the configuration and performance of the glazing to be installed, considering the SHGC of the entire glazing system.

scholarly journals Heating and Cooling Load of Building according to Atrium Layout

KIEAE Journal ◽  
2016 ◽  
Vol 16 (1) ◽  
pp. 29-36
Author(s):  
Nam-Young Jeong ◽  
Ji-Young Lee ◽  
Young Tae Chae
Keyword(s):  
Cooling Load ◽  
Heating And Cooling ◽  
Heating And Cooling Load

scholarly journals Full-scale operation of a novel two-pipe active beam system for simultaneous heating and cooling of office buildings

2019 ◽  
Vol 111 ◽  
pp. 01073
Author(s):  
Alessandro Maccarini ◽  
Göran Hultmark ◽  
Niels C. Bergsøe ◽  
Alireza Afshari
Keyword(s):  
Room Temperature ◽  
Energy Performance ◽  
Physical Parameters ◽  
Heating And Cooling ◽  
System A ◽  
A Value ◽  
Beam System ◽  
Simultaneous Heating ◽  
Simultaneous Heating And Cooling ◽  
Cooling Loads

This paper presents an investigation on the operation of a novel active beam system installed in an office building located in Jönköping, Sweden. The system consists of two parts: a dedicated outdoor air system (DOAS) to satisfy latent loads and ventilation requirements, and a water circuit to meet sensible heating and cooling loads. The novelty of the system is in relation to the water circuit, which is able to provide simultaneous heating and cooling through a single water loop that is near the room temperature. The energy performance of the system is currently being monitored through a number of sensors placed along the water circuit. Relevant physical parameters are being measured and data are available through a monitoring system. A preliminary analysis shows that the system is performing as designed. Results are shown for a typical week in winter, spring and summer. In particular, the supply water temperature in the circuit was between 20°C (in summer) and 23.2°C (in winter). The maximum supply/return temperature difference was found in summer and it assumed a value of 1.5 K. It is noticed that in spring supply and return water temperatures almost overlap.

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