scholarly journals Use of Stochastic Weather Generators in the Projection of Building Energy Demand in a Changing Climate

2011 ◽  
Author(s):  
David R.S. Williams ◽  
Lucia Elghali ◽  
Russel C. Wheeler
Keyword(s):  
Energy Demand ◽  
Building Energy ◽  
Changing Climate ◽  
Weather Generators ◽  
Stochastic Weather Generators

scholarly journals A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 156
Author(s):  
Paige Wenbin Tien ◽  
Shuangyu Wei ◽  
John Calautit
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Energy Demand ◽  
Energy Savings ◽  
Internal Heat ◽  
Electrical Equipment ◽  
Building Energy ◽  
Hvac Systems ◽  
Equipment Usage

Because of extensive variations in occupancy patterns around office space environments and their use of electrical equipment, accurate occupants’ behaviour detection is valuable for reducing the building energy demand and carbon emissions. Using the collected occupancy information, building energy management system can automatically adjust the operation of heating, ventilation and air-conditioning (HVAC) systems to meet the actual demands in different conditioned spaces in real-time. Existing and commonly used ‘fixed’ schedules for HVAC systems are not sufficient and cannot adjust based on the dynamic changes in building environments. This study proposes a vision-based occupancy and equipment usage detection method based on deep learning for demand-driven control systems. A model based on region-based convolutional neural network (R-CNN) was developed, trained and deployed to a camera for real-time detection of occupancy activities and equipment usage. Experiments tests within a case study office room suggested an overall accuracy of 97.32% and 80.80%. In order to predict the energy savings that can be attained using the proposed approach, the case study building was simulated. The simulation results revealed that the heat gains could be over or under predicted when using static or fixed profiles. Based on the set conditions, the equipment and occupancy gains were 65.75% and 32.74% lower when using the deep learning approach. Overall, the study showed the capabilities of the proposed approach in detecting and recognising multiple occupants’ activities and equipment usage and providing an alternative to estimate the internal heat emissions.

scholarly journals Modeling and Optimization of Smart Building Energy Management System Considering Both Electrical and Thermal Load

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 574
Author(s):  
Muhammad Hilal Khan ◽  
Azzam Ul Asar ◽  
Nasim Ullah ◽  
Fahad R. Albogamy ◽  
Muhammad Kashif Rafique
Keyword(s):  
Energy Management ◽  
Management System ◽  
Energy Demand ◽  
Storage System ◽  
Building Energy ◽  
Energy Management System ◽  
Smart Building ◽  
Building Energy Management ◽  
Real Coded Genetic Algorithm ◽  
The Impact

Energy consumption in buildings is expected to increase by 40% over the next 20 years. Electricity remains the largest source of energy used by buildings, and the demand for it is growing. Building energy improvement strategies is needed to mitigate the impact of growing energy demand. Introducing a smart energy management system in buildings is an ambitious yet increasingly achievable goal that is gaining momentum across geographic regions and corporate markets in the world due to its potential in saving energy costs consumed by the buildings. This paper presents a Smart Building Energy Management system (SBEMS), which is connected to a bidirectional power network. The smart building has both thermal and electrical power loops. Renewable energy from wind and photo-voltaic, battery storage system, auxiliary boiler, a fuel cell-based combined heat and power system, heat sharing from neighboring buildings, and heat storage tank are among the main components of the smart building. A constraint optimization model has been developed for the proposed SBEMS and the state-of-the-art real coded genetic algorithm is used to solve the optimization problem. The main characteristics of the proposed SBEMS are emphasized through eight simulation cases, taking into account the various configurations of the smart building components. In addition, EV charging is also scheduled and the outcomes are compared to the unscheduled mode of charging which shows that scheduling of Electric Vehicle charging further enhances the cost-effectiveness of smart building operation.

scholarly journals IMPLICATION OF INFILTRATION ON BUILDING ENERGY DEMAND - A REVIEW OF VERIFICATION METHODS

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Nevena S. Lukić ◽  
Ljiljana Đukanovic ◽  
Ana Radivojević
Keyword(s):  
Energy Efficiency ◽  
Energy Demand ◽  
Housing Stock ◽  
Building Energy ◽  
Verification Methods ◽  
Occupant Comfort ◽  
Standards And Regulations ◽  
Considerable Impact ◽  
Current Standards ◽  
The Eu

Infiltration has a considerable impact on both, energy efficiency and occupant comfort in buildings. Due to the complexity of the analysis of this phenomenon in buildings, the verification methods are very important for its diagnostics and evaluation. In this paper, the matter of infiltration in buildings is being considered referring to both, calculation models and methods, as well as through current standards and regulations in the EU and Serbia. Different valorization methods are presented and analyzed regarding their characteristics, applicability, and complexity. Finally, preliminary infiltration measurements with a pressurization test, conducted on selected buildings of Belgrade housing stock are presented and compared with values defined by the current regulations in Serbia. Results pointed out current problems and the need for improvements regarding the treatment of infiltration in local regulations and practice.

scholarly journals Risks and opportunities for a Swiss hydropower company in a changing climate

2019 ◽  
Author(s):  
Kirsti Hakala ◽  
Nans Addor ◽  
Thibault Gobbe ◽  
Johann Ruffieux ◽  
Jan Seibert
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Energy Demand ◽  
Climate Change Impacts ◽  
Climate Impacts ◽  
Water Volume ◽  
Volume Distribution ◽  
Impact Studies ◽  
Changing Climate ◽  
Low Flows

Abstract. Anticipating and adapting to climate change impacts on water resources requires a detailed understanding of future hydroclimatic changes and of stakeholders' vulnerability to these changes. However, climate change impact studies are often conducted at a spatial scale that is too coarse to capture the specificity of individual catchments, and more importantly, the changes they focus on are not necessarily the changes most critical to stakeholders. While recent studies have combined hydrological and electricity market modeling, they tend to aggregate all climate impacts by focusing solely on reservoir profitability, and thereby provide limited insights into climate change adaptation. Here, we collaborated with Groupe E, a hydropower company operating several reservoirs in the Swiss pre-Alps and worked with them to produce hydroclimatic projections tailored to support their upcoming water concession negotiations. We started by identifying the vulnerabilities of their activities to climate change and then together chose streamflow and energy indices to characterize the associated risks. We provided Groupe E with figures showing the projected climate change impacts, which were refined over several meetings. The selected indices enabled us to simultaneously assess a variety of impacts induced by changes on i) the seasonal water volume distribution, ii) low flows, iii) high flows, and iv) energy demand. We were hence able to identify key opportunities (e.g., the future increase of reservoir inflow in winter, when electricity prices are historically high) and risks (e.g., the expected increase of consecutive days of low flows in summer and fall, which is likely to make it more difficult to meet residual flow requirements). This study highlights that the hydrological opportunities and risks associated with reservoir management in a changing climate depend on a range of factors beyond those covered by traditional impact studies. We also illustrate the importance of identifying stakeholder needs and using them to inform the production of climate impact projections. Our user-centered approach is transferable to other impact modeling studies, in the field of water resources and beyond.

An Energy-Related CityGML ADE and Its Application for Heating Demand Calculation

2019 ◽  
pp. 1306-1323
Author(s):  
Marcel Bruse ◽  
Romain Nouvel ◽  
Parag Wate ◽  
Volker Kraut ◽  
Volker Coors
Keyword(s):  
Energy Demand ◽  
Building Energy ◽  
Energy Simulation ◽  
Data Sets ◽  
Application Domain ◽  
Data Formats ◽  
Building Energy Systems ◽  
Domain Extension ◽  
Energy Simulations ◽  
Heating Energy Demand

Different associated properties of city models like building geometries, building energy systems, building end uses, and building occupant behavior are usually saved in different data formats and are obtained from different data sources. Experience has shown that the integration of these data sets for the purpose of energy simulation on city scale is often cumbersome and error prone. A new application domain extension for CityGML has been developed in order to integrate energy-related figures of buildings, thermal volumes, and facades with their geometric descriptions. These energy-related figures can be parameters or results of energy simulations. The applicability of the new application domain extension has been demonstrated for heating energy demand calculation.

scholarly journals Impact Assessment for Building Energy Models Using Observed vs. Third-Party Weather Data Sets

2020 ◽  
Vol 12 (17) ◽  
pp. 6788 ◽  
Author(s):  
Eva Lucas Segarra ◽  
Germán Ramos Ruiz ◽  
Vicente Gutiérrez González ◽  
Antonis Peppas ◽  
Carlos Fernández Bandera
Keyword(s):  
Energy Demand ◽  
Meteorological Data ◽  
Building Energy ◽  
Third Party ◽  
Weather Data ◽  
Data Sets ◽  
Weather Station ◽  
Energy Models ◽  
The Impact ◽  
Energy Strategies

The use of building energy models (BEMs) is becoming increasingly widespread for assessing the suitability of energy strategies in building environments. The accuracy of the results depends not only on the fit of the energy model used, but also on the required external files, and the weather file is one of the most important. One of the sources for obtaining meteorological data for a certain period of time is through an on-site weather station; however, this is not always available due to the high costs and maintenance. This paper shows a methodology to analyze the impact on the simulation results when using an on-site weather station and the weather data calculated by a third-party provider with the purpose of studying if the data provided by the third-party can be used instead of the measured weather data. The methodology consists of three comparison analyses: weather data, energy demand, and indoor temperature. It is applied to four actual test sites located in three different locations. The energy study is analyzed at six different temporal resolutions in order to quantify how the variation in the energy demand increases as the time resolution decreases. The results showed differences up to 38% between annual and hourly time resolutions. Thanks to a sensitivity analysis, the influence of each weather parameter on the energy demand is studied, and which sensors are worth installing in an on-site weather station are determined. In these test sites, the wind speed and outdoor temperature were the most influential weather parameters.

Sign in / Sign up

Export Citation Format

Share Document