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.

Forecasting Building Energy Demands for New York City With a Coupled Weather-Building Energy Model

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

Major 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 and customers may incur in additional charges for peak load management in regulated demand-response markets. This opens the need for the development of analytical tools that can inform building managers and utilities about near future conditions so they are better able to avoid peak demand charges, reducing 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 forecast is based on the Weather Research and Forecast model version 3.5, coupled with a building environment parameterization and building energy model. 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-hr city-scale weather and energy forecasts show good agreement with the archived data from both weather stations records and energy records by NYISO.

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.

The Impact of Pavement Albedo on Radiative Forcing and Building Energy Demand: Comparative Analysis of Urban Neighborhoods

2018 ◽  
Vol 2672 (40) ◽  
pp. 88-96
Author(s):  
Xin Xu ◽  
Jeremy Gregory ◽  
Randolph Kirchain
Keyword(s):  
Global Warming ◽  
Comparative Analysis ◽  
Radiative Forcing ◽  
Energy Demand ◽  
Building Energy ◽  
Urban Neighborhoods ◽  
Urban Morphology ◽  
Model Framework ◽  
The Impact ◽  
Reflective Surfaces

Albedo is the measure of the ratio of solar radiation reflected by the Earth’s surface. High-albedo reflective surfaces absorb less energy and reflect more shortwave radiation. The change in radiative energy balance at the top-of-atmosphere (TOA), which is called radiative forcing (RF), reduces nearby air temperatures and influences the surrounding building energy demand (BED). The impact of reflective surfaces on RF and BED has been investigated separately by researchers through modeling and observational studies, however, no one has compared RF and BED impacts under the same context and the net effect of these two phenomena remains unclear. This paper presents a comprehensive approach to assess the net impacts of pavement albedo modification strategies in selected urban neighborhoods. We apply an adapted analytical model for RF and a hybrid model framework combining two different models for BED to estimate the impacts of increasing pavement albedo from 0.1 to 0.3 for different urban neighborhoods in Boston and Phoenix. The impact of several context-specific factors, including location, urban morphology, shadings etc., are taken into account in the models. Comparative analysis reveals that the net impact of changing pavement albedo can vary from one neighborhood to another. In Phoenix downtown, reflective pavements create net global warming potential burdens, while increasing pavement albedo results in potential savings in the Boston downtown area. This work provides insights into pavement albedo impacts at urban scale and supports more informed decisions on pavement designs that save energy and counteract some of the effects of global warming.

Microclimate and urban morphology effects on building energy demand in different European cities

2020 ◽  
Vol 224 ◽  
pp. 110129
Author(s):  
A. Boccalatte ◽  
M. Fossa ◽  
L. Gaillard ◽  
C. Menezo
Keyword(s):  
Energy Demand ◽  
Building Energy ◽  
Urban Morphology ◽  
European Cities

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.

Building Peak Load Management With High Resolution Weather Data

2015 ◽  
Author(s):  
Yehisson Tibana ◽  
Estatio Gutierrez ◽  
M. Arend ◽  
J. E. Gonzalez
Keyword(s):  
High Resolution ◽  
Weather Forecasting ◽  
Peak Load ◽  
Building Energy ◽  
Energy Model ◽  
Department Of Energy ◽  
Urban Heat Islands ◽  
Weather Data ◽  
Load Management ◽  
Peak Loads

Dense urban environments are exposed to the combined effects of rising global temperatures and urban heat islands. This combination is resulting in increasing trends of energy consumption in cities, associated mostly with air conditioning to maintain indoor human comfort conditions. During periods of extreme summer weather, electrical usage usually reaches peak loads, stressing the electrical grid. The purpose of this study is to explore the use of available, high resolution weather data by effectively preparing a building for peak load management. The subject of study is a 14 floor, 620,782 sq ft building located in uptown Manhattan, New York City (40.819257 N, −73.949288 W). To precisely quantify thermal loads of the buildings for the summer conditions; a single building energy model (SBEM), the US Department of Energy EnergyPlus™ was used. The SBEM was driven by a weather file built from weather data of the urbanized weather forecasting model (uWRF), a high resolution weather model coupled to a building energy model. The SBEM configuration and simulations were calibrated with winter actual gas and electricity data using 2010 as the benchmark year. In order to show the building peak load management, demand response techniques and technologies were implemented. The methods used to prepare the building included generator usage during high peak loads and use of a thermal storage system. An ensemble of cases was analyzed using current practice, use of high resolution weather data, and use of building preparation technologies. Results indicated an average summer peak savings of more than 30% with high resolution weather data.

scholarly journals Hacia un modelo dinámico para la isla de calor urbana de Madrid = Towards a Dynamic Model for the Urban Heat Island of Madrid.

2016 ◽  
Vol 2 (1) ◽  
pp. 49 ◽  
Author(s):  
Miguel Núñez Peiró ◽  
Emilia Román López ◽  
Carmen Sánchez-Guevara Sánchez ◽  
Francisco Javier Neila González
Keyword(s):  
Urban Heat Island ◽  
Predictive Model ◽  
Energy Demand ◽  
Building Energy ◽  
Energy Performance ◽  
Heat Island ◽  
Urban Heat ◽  
Weather Stations ◽  
Energy Simulations ◽  
The City

Resumen Esta investigación se enmarca dentro del proyecto MODIFICA (modelo predictivo - Edificios - Isla de Calor Urbano), financiado por el Programa de I + D + i Orientada a los Retos de la sociedad 'Retos Investigación' de 2013. Está dirigido a desarrollar un modelo predictivo de eficiencia energética para viviendas, bajo el efecto de isla de calor urbano (AUS) con el fin de ponerla en práctica en la evaluación de la demanda de energía real y el consumo en las viviendas. A pesar de los grandes avances que se han logrado durante los últimos años en el rendimiento energético de edificios, los archivos de tiempo utilizados en la construcción de simulaciones de energía se derivan generalmente de estaciones meteorológicas situadas en las afueras de la ciudad. Por lo tanto, el efecto de la Isla de Calor Urbano (ICU) no se considera en estos cálculos, lo que implica una importante falta de precisión. Centrado en explorar cómo incluir los fenómenos ICU, el presente trabajo recopila y analiza la dinámica por hora de la temperatura en diferentes lugares dentro de la ciudad de Madrid. Abstract This research is framed within the project MODIFICA (Predictive model - Buildings - Urban Heat Island), funded by Programa de I+D+i orientada a los retos de la sociedad 'Retos Investigación' 2013. It is aimed at developing a predictive model for dwelling energy performance under the Urban Heat Island (UHI) effect in order to implement it in the evaluation of real energy demand and consumption in dwellings. Despite great advances on building energy performance have been achieved during the last years, weather files used in building energy simulations are usually derived from weather stations placed in the outskirts of the city. Hence, Urban Heat Island (UHI) effect is not considered in this calculations, which implies an important lack of accuracy. Focused on exploring how to include the UHI phenomena, the present paper compiles and analyses the hourly dynamics of temperature in different locations within the city of Madrid. 

Uncertainty and Sensitivity Analysis of Building Energy Demand Model Based on Occupant Behaviors

2011 ◽  
Vol 71-78 ◽  
pp. 411-415
Author(s):  
Zhi Bin Zhao ◽  
Wei Sheng Xu ◽  
Da Zhang Cheng
Keyword(s):  
Sensitivity Analysis ◽  
Real Time ◽  
Energy Demand ◽  
Dynamic Network ◽  
Building Energy ◽  
Demand Model ◽  
Model Framework ◽  
Basic Model ◽  
Dynamic Relation ◽  
Input Parameters

This paper attempts to illustrate Uncertainty Analysis (UA) and Sensitivity Analysis (SA) of real-time building energy demand model, which is derived from the dynamic relation of occupant behaviors and building space. UA and SA are indispensable sections of system development to insure the efficiency and accuracy of the model while there are three essential stage of UA and SA we followed. In terms of UA and SA, it is possible to structure a rational framework of complex dynamic network model, and discover the mapping between building energy demand and particular relation network patterns. We assume, firstly, a multi-mode dynamic relation networks model of occupant behavior, building space and temporal unit tends to be developed, and the definitions of basic model framework are given. Then, in the cases of the definitions in the basic model framework assumption, the propagation of uncertainty is taken into consideration according to the sampling based methods mapping the input parameters patterns onto the predictable results. Finally, we discuss the determination of sensitivity analysis with Morris method and Variance-based methods. In this paper, via UA and SA, our goal is to optimize the mapping procedure of the Dynamic Network Analysis (DNA) in building energy demand model, explore the essential input parameters pattern, and improve the precision of real-time model prediction.

An integrated model for quantifying the impacts of pavement albedo and urban morphology on building energy demand

2020 ◽  
Vol 211 ◽  
pp. 109759 ◽  
Author(s):  
Xin Xu ◽  
Hessam AzariJafari ◽  
Jeremy Gregory ◽  
Leslie Norford ◽  
Randolph Kirchain
Keyword(s):  
Energy Demand ◽  
Building Energy ◽  
Integrated Model ◽  
Urban Morphology
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