scholarly journals Designing an Energy-Resilient Neighbourhood Using an Urban Building Energy Model

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4445
Author(s):  
Niall Buckley ◽  
Gerald Mills ◽  
Samuel Letellier-Duchesne ◽  
Khadija Benis
Keyword(s):  
Energy Demand ◽  
Building Energy ◽  
Energy Model ◽  
Land Uses ◽  
Building Stock ◽  
Sources And Sinks ◽  
Urban Modelling ◽  
Zero Carbon ◽  
Land Covers ◽  
Test Scenarios

A climate resilient city, perforce, has an efficient and robust energy infrastructure that can harvest local energy resources and match energy sources and sinks that vary over space and time. This paper explores the use of an urban building energy model (UBEM) to examine the potential for creating a near-zero carbon neighbourhood in Dublin (Ireland) that is characterised by diverse land-uses and old and new building stock. UBEMs are a relatively new tool that allows the simulation of building energy demand across an urbanised landscape and can account for building layout, including the effects of overshadowing and the potential for facade retrofits and energy generation. In this research, a novel geographic database of buildings is created using archetypes, and the associated information on dimensions, fabric and energy systems is integrated into the Urban Modelling Interface (UMI). The model is used to simulate current and future energy demand based on climate change projections and to test scenarios that apply retrofits to the existing stock and that link proximate land-uses and land-covers. The latter allows a significant decoupling of the neighbourhood from an offsite electricity generation station with a high carbon output. The findings of this paper demonstrate that treating neighbourhoods as single energy entities rather than collections of individual sectors allows the development of bespoke carbon reducing scenarios that are geographically situated. The work shows the value of a neighbourhood-based approach to energy management using UBEMs.

scholarly journals Investigating Bundled Approaches To Reduce Greenhouse Gas Emissions in the Toronto 2030 District

2021 ◽  
Author(s):  
Patrick Ritsma
Keyword(s):  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Energy Demand ◽  
Building Energy ◽  
Energy Performance ◽  
Energy Reduction ◽  
Building Stock ◽  
Gas Emissions ◽  
Urban Core ◽  
Energy Models

Building energy models are an effective tool for evaluating energy reduction opportunities in both design phase and post-occupancy scenarios. By merging building energy models with city scale building stock data, it is possible to analyze energy performance at a greater breadth, providing more informed policy decisions and solutions to energy demand asymmetries in urban metropolises. This study examines the energy reduction potential for office buildings in the Toronto 2030 District, by testing individual and bundled energy conservation measures and greenhouse gas reduction strategies using a reference building energy model. When extrapolated across Toronto’s urban core, simulation results determined that standard interventions on the existing office building stock have the potential to reduce greenhouse gas emissions by as much as 91.5%, in line with 2030 District initiatives.

Forecasting Building Energy Demands With a Coupled Weather-Building Energy Model in a Dense Urban Environment

2016 ◽  
Vol 139 (1) ◽  
Author(s):  
Luis E. Ortiz ◽  
Jorge E. Gonzalez ◽  
Estatio Gutierrez ◽  
Mark Arend
Keyword(s):  
New York ◽  
Energy Demand ◽  
Building Energy ◽  
Urban Morphology ◽  
Energy Model ◽  
Demand Model ◽  
Peak Demand ◽  
Peak Loads ◽  
Extreme Heat Event ◽  
The City

Major new metropolitan centers experience challenges during management of peak electrical loads, typically occurring during extreme summer events. These peak loads expose the reliability of the electrical grid on the production and transmission side, while customers may incur considerable charges from increased metered peak demand, failing to meet demand response program obligations, or both. These challenges create a need for analytical tools that can inform building managers and utilities about near future conditions so they are better able to avoid peak demand charges and reduce building operational costs. In this article, we report on a tool and methodology to forecast peak loads at the city scale using New York City (NYC) as a test case. The city of New York experiences peak electric demand loads that reach up to 11 GW during the summertime, and are projected to increase to over 12 GW by 2025, as reported by the New York Independent System Operator (NYISO). The energy forecast is based on the Weather Research and Forecast (WRF) model version 3.5, coupled with a multilayer building energy model (BEM). Urban morphology parameters are assimilated from the New York Primary Land Use Tax-Lot Output (PLUTO), while the weather component of the model is initialized daily from the North American Mesoscale (NAM) model. A city-scale analysis is centered in the summer months of June–July 2015 which included an extreme heat event (i.e., heat wave). The 24-h city-scale weather and energy forecasts show good agreement with the archived data from both weather stations records and energy records by NYISO. This work also presents an exploration of space cooling savings from the use of white roofs as an application of the city-scale energy demand model.

scholarly journals Comparison of nZEB indicators for hotel renovations under different European climatic conditions

2020 ◽  
Author(s):  
Filipe O Cunha ◽  
Armando C Oliveira
Keyword(s):  
Energy Efficiency ◽  
Energy Use ◽  
Minimum Energy ◽  
Building Energy ◽  
Energy Performance ◽  
Climatic Conditions ◽  
Energy Model ◽  
The European Union ◽  
Building Stock ◽  
Efficiency Measures

Abstract Hotels hold an important role in the energy efficiency policies of the European Union (EU), as they are typically ranked among the top energy consumers in the non-residential sector. However, a significant amount of the energy used in hotels is wasted, leaving ample room for enhancing energy-efficiency and resource conservation. Indeed, energy refurbishment of the hotel building stock is crucial in order to reach the nearly zero energy building (nZEB) status imposed by EU Directives for energy efficiency, and also an important pillar to achieve the energy targets for 2030 and the transition towards climate-neutral levels by 2050. A typical 4-star hotel in operation in Faro (Portugal) was used as a case study in order to establish energy performance indicators for nZEB hotels in three European cities, taking into account the influence of the climatic context, the technical feasibility and cost effectiveness of the best energy retrofit packages. The study started after the calibration of the building energy model by means of an energy audit and measured data, in order to have a baseline model that represents well the actual energy use of the hotel in the reference location. The building energy model was developed by using DesignBuilder/EnergyPlus software. The validated model was then used to assess the effect of the best retrofit interventions (energy efficiency measures and active solar systems) in order to set minimum energy performance requirements and to reach cost-optimal levels and nZEB levels for refurbished hotels. A significant energy-saving potential was found for the cost-optimal benchmarks, and the obtained nZEB levels can be achieved under technically and economically conditions for the selected cities: Faro, London and Athens.

scholarly journals On the Spatio-Temporal End-User Energy Demands of a Dense Urban Environment

2017 ◽  
Vol 139 (4) ◽  
Author(s):  
Krarti Ahmed ◽  
Luis E. Ortiz ◽  
J. E. González
Keyword(s):  
New York ◽  
Energy Demand ◽  
Demand Forecasting ◽  
Warm Season ◽  
Weather Forecast ◽  
Building Energy ◽  
Energy Model ◽  
Simulation Software ◽  
Weather Data ◽  
The City

Buildings in major metropolitan centers face increased peak electrical load during the warm season, especially during extreme heat events. City-wide, the increased demand for space cooling can stress the grid, increasing generation costs. It is therefore imperative to better understand building energy consumption profiles at the city scale. This understanding is not only paramount for users to avoid peak demand charges but also for utilities to improve load management. This study aims to develop a city-scale energy demand forecasting tool using high resolution weather data interfaced with a single building energy model. The forecasting tool was tested in New York City (NYC) due to the availability of building morphology data. We identified 51 building archetypes, based on the building function (residential, educational, or office), the age of the building, and the land use type. The single building simulation software used is energyplus which was coupled to an urbanized weather research and forecasting (uWRF) model for weather forecast input. Individual buildings were linked to the archetypes and scaled using the building total floor area. The single building energy model is coupled to the weather model resulting in energy maps of the city. These maps provide an energy end-use profile for NYC for total and individual components including lighting, equipment and heating, ventilation, and air-conditioning (HVAC). The methodology was validated with single building energy data for a particular location, and with city-scale electric load archives, showing good agreements in both cases.

scholarly journals A GIS-Based Procedure for Estimating the Energy Demand Profiles of Buildings towards Urban Energy Policies

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5445 ◽  
Author(s):  
Simone Ferrari ◽  
Federica Zagarella ◽  
Paola Caputo ◽  
Giuliano Dall’O’
Keyword(s):  
Energy Demand ◽  
Simulation Models ◽  
Building Energy ◽  
Building Stock ◽  
Dynamic Simulations ◽  
Existing Building ◽  
Efficiency Measures ◽  
Smart Energy Systems ◽  
Urban Energy ◽  
Definition Of

Assessing the existing building stock’s hourly energy demand and predicting its variation due to energy efficiency measures are fundamental for planning strategies towards renewable-based Smart Energy Systems. However, the need for accurate methods for this purpose in the literature arises. The present article describes a GIS-based procedure developed for estimating the energy demand profiles of urban buildings based on the definition of the volumetric consistency of a building stock, characterized by different ages of construction and the most widespread uses, as well as dynamic simulations of a set of Building Energy Models adopting different energy-related features. The simulation models are based on a simple Building Energy Concept where selected thermal zones, representative of different boundary conditions options, are accounted. By associating the simulated hourly energy density profiles to the geo-referenced building stock and to the surveyed thermal system types, the whole hourly energy profile is estimated for the considered area. The method was tested on the building stock of Milan (Italy) and validated with the data available from the annual energy balance of the city. This procedure could support energy planners in defining urban energy demand profiles for energy policy scenarios.

scholarly journals Investigating Bundled Approaches To Reduce Greenhouse Gas Emissions in the Toronto 2030 District

2021 ◽  
Author(s):  
Patrick Ritsma
Keyword(s):  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Energy Demand ◽  
Building Energy ◽  
Energy Performance ◽  
Energy Reduction ◽  
Building Stock ◽  
Gas Emissions ◽  
Urban Core ◽  
Energy Models

Building energy models are an effective tool for evaluating energy reduction opportunities in both design phase and post-occupancy scenarios. By merging building energy models with city scale building stock data, it is possible to analyze energy performance at a greater breadth, providing more informed policy decisions and solutions to energy demand asymmetries in urban metropolises. This study examines the energy reduction potential for office buildings in the Toronto 2030 District, by testing individual and bundled energy conservation measures and greenhouse gas reduction strategies using a reference building energy model. When extrapolated across Toronto’s urban core, simulation results determined that standard interventions on the existing office building stock have the potential to reduce greenhouse gas emissions by as much as 91.5%, in line with 2030 District initiatives.

scholarly journals Experimental Data and Simulations of Performance and Thermal Comfort in a Typical Mediterranean House

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3311
Author(s):  
Víctor Pérez-Andreu ◽  
Carolina Aparicio-Fernández ◽  
José-Luis Vivancos ◽  
Javier Cárcel-Carrasco
Keyword(s):  
Energy Efficiency ◽  
Thermal Comfort ◽  
Energy Demand ◽  
Natural Ventilation ◽  
Thermal Characterization ◽  
Energy Performance ◽  
Building Stock ◽  
Energy Demands ◽  
Dwelling House ◽  
Account Standard

The number of buildings renovated following the introduction of European energy-efficiency policy represents a small number of buildings in Spain. So, the main Spanish building stock needs an urgent energy renovation. Using passive strategies is essential, and thermal characterization and predictive tests of the energy-efficiency improvements achieving acceptable levels of comfort for their users are urgently necessary. This study analyzes the energy performance and thermal comfort of the users in a typical Mediterranean dwelling house. A transient simulation has been used to acquire the scope of Spanish standards for its energy rehabilitation, taking into account standard comfort conditions. The work is based on thermal monitoring of the building and a numerical validated model developed in TRNSYS. Energy demands for different models have been calculated considering different passive constructive measures combined with real wind site conditions and the behavior of users related to natural ventilation. This methodology has given us the necessary information to decide the best solution in relation to energy demand and facility of implementation. The thermal comfort for different models is not directly related to energy demand and has allowed checking when and where the measures need to be done.

scholarly journals Saving energy in residential buildings: the role of energy pricing

2021 ◽  
Vol 167 (1-2) ◽  
Author(s):  
Jens Ewald ◽  
Thomas Sterner ◽  
Eoin Ó Broin ◽  
Érika Mata
Keyword(s):  
Energy Demand ◽  
Income Elasticity ◽  
Residential Buildings ◽  
Policy Framework ◽  
Energy Prices ◽  
Residential Energy ◽  
Residential Energy Demand ◽  
Long Run ◽  
Zero Carbon

AbstractA zero-carbon society requires dramatic change everywhere including in buildings, a large and politically sensitive sector. Technical possibilities exist but implementation is slow. Policies include many hard-to-evaluate regulations and may suffer from rebound mechanisms. We use dynamic econometric analysis of European macro data for the period 1990–2018 to systematically examine the importance of changes in energy prices and income on residential energy demand. We find a long-run price elasticity of −0.5. The total long-run income elasticity is around 0.9, but if we control for the increase in income that goes towards larger homes and other factors, the income elasticity is 0.2. These findings have practical implications for climate policy and the EU buildings and energy policy framework.

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.

Sign in / Sign up

Export Citation Format

Share Document