Related Papers
Intelligent Industrial Systems
Energy Semantic Network for Building Energy Management
2015 •
Ahmed Eldessouky
Smart Grid and Renewable Energy
Comparison and Simulation of Building Thermal Models for Effective Energy Management
2015 •
fatima amara
Journal of Energy
Energy Consumption Analysis of a Large Building at Memorial University
2019 •
M. Tariq Iqbal
Spread in energy use in buildings dependent on choice of heating and ventilation system
2015 •
Paula Wahlgren
Bringing an understanding of energy use and energy efficiency in buildings to the next level
2018 •
Delano Wouters
This thesis will focus on bringing understanding of energy use and energy efficiency in buildings to the next level. Buildings represent a higher percentage of energy consumption compared to other economic sectors. Improved control of energy use can lead to a higher energy efficiency in non-residential buildings. Long term energy savings can be achieved by improving the building design as well as conserving energy during the operation phase. It is therefore beneficial to study how currently available big data about non-residential buildings can be used to give more knowledge and insight in the energy consumption of these buildings. This study will use the methodology of an exploratory case study with as a study design the framework of an energy analysis. The entity studied in this case is the library of the TU Delft, the BTUD. The energy analysis is a five-step framework and is used to investigate the energy flow inside the building. The big data streams which are gathered and resea...
Energy Procedia
A simulation exercise to improve building energy performance characterization via on-board monitoring
2017 •
Glenn Reynders
Energies
Comparison between Energy Simulation and Monitoring Data in an Office Building
2021 •
Matteo Porta
One of the most important steps in the retrofitting process of a building is to understand its pre-retrofitting stage energy performance. The best choice for carrying this out is by means of a calibrated building energy simulation (BES) model. Then, the testing of different retrofitting solutions in the validated model allows for quantifying the improvements that may be obtained, in order to choose the most suitable solution. In this work, based on the available detailed building drawings, constructive details, building operational data and the data sets obtained on a minute basis (for a whole year) from a dedicated energy monitoring system, the calibration of an in-use office building energy model has been carried out. It has been possible to construct a detailed white box model based on Design Builder software. Then, comparing the model output for indoor air temperature, lighting consumption and heating consumption against the monitored data, some of the building envelope paramete...
Energy Efficiency: Prediction of the Heat and Cooling Requirements of Buildings
Asif Rabbani
The aim is to develop an artificial neural network to see the best training parameters that efficiently predict the energy efficiency. We will be using input parameters including relative compactness, surface area, wall area, roof area, over all height, orientation and glazing area and out parameters such as heating and cooling load to develop an artificial neural network (ANN) to predict the energy consumption in buildings of north Cyprus during the early stage of architectural design. ANN was obtained by analyzing the output variables and number of hidden layer neurons. Results showed ANN with multiple outputs provide better prediction performance.
Disaggregating And Forecasting The Total Energy Consumption Of A Building: A Case Study Of A High Cooling Demand Facility
2016 •
Khashayar Mahani
Energy disaggregation has been focused by many energy companies since energy efficiency can be achieved when the breakdown of energy consumption is known. Companies have been investing in technologies to come up with software and/or hardware solutions that can provide this type of information to the consumer. On the other hand, not all people can afford to have these technologies. Therefore, in this paper, we present a methodology for breaking down the aggregate consumption and identifying the highdemanding end-uses profiles. These energy profiles will be used to build the forecast model for optimal control purpose. A facility with high cooling load is used as an illustrative case study to demonstrate the results of proposed methodology. We apply a high level energy disaggregation through a pattern recognition approach in order to extract the consumption profile of its rooftop packaged units (RTUs) and present a forecast model for the energy consumption.
Automation in Construction
Data visualization and analysis of energy flow on a multi-zone building scale
aly abdelalim
