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.

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 Methodology for the Quantification of the Impact of Weather Forecasts in Predictive Simulation Models

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1309 ◽  
Author(s):  
Eva Lucas Segarra ◽  
Hu Du ◽  
Germán Ramos Ruiz ◽  
Carlos Fernández Bandera
Keyword(s):  
Energy Demand ◽  
Forecast Error ◽  
Weather Forecast ◽  
Building Energy ◽  
Weather Data ◽  
Weather Forecasts ◽  
Hourly Data ◽  
The Future ◽  
The Impact ◽  
Near Future

The use of Building Energy Models (BEM) has become widespread to reduce building energy consumption. Projection of the model in the future to know how different consumption strategies can be evaluated is one of the main applications of BEM. Many energy management optimization strategies can be used and, among others, model predictive control (MPC) has become very popular nowadays. When using models for predicting the future, we have to assume certain errors that come from uncertainty parameters. One of these uncertainties is the weather forecast needed to predict the building behavior in the near future. This paper proposes a methodology for quantifying the impact of the error generated by the weather forecast in the building’s indoor climate conditions and energy demand. The objective is to estimate the error introduced by the weather forecast in the load forecasting to have more precise predicted data. The methodology employed site-specific, near-future forecast weather data obtained through online open access Application Programming Interfaces (APIs). The weather forecast providers supply forecasts up to 10 days ahead of key weather parameters such as outdoor temperature, relative humidity, wind speed and wind direction. This approach uses calibrated EnergyPlus models to foresee the errors in the indoor thermal behavior and energy demand caused by the increasing day-ahead weather forecasts. A case study investigated the impact of using up to 7-day weather forecasts on mean indoor temperature and energy demand predictions in a building located in Pamplona, Spain. The main novel concepts in this paper are: first, the characterization of the weather forecast error for a specific weather data provider and location and its effect in the building’s load prediction. The error is calculated based on recorded hourly data so the results are provided on an hourly basis, avoiding the cancel out effect when a wider period of time is analyzed. The second is the classification and analysis of the data hour-by-hour to provide an estimate error for each hour of the day generating a map of hourly errors. This application becomes necessary when the building takes part in the day-ahead programs such as demand response or flexibility strategies, where the predicted hourly load must be provided to the grid in advance. The methodology developed in this paper can be extrapolated to any weather forecast provider, location or building.

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 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.

Four Key Concepts for Studying Context-based Compositions

2017 ◽  
Vol 23 (1) ◽  
pp. 3-11
Author(s):  
Anette Vandsø
Keyword(s):  
New York ◽  
World Trade Center ◽  
Dark Side ◽  
Weather Data ◽  
Specific Context ◽  
Hurricane Floyd ◽  
Key Concepts ◽  
Ruhr District ◽  
Trade Center ◽  
The City

This theoretical article investigates context-based compositions where we cannot identify the real-world context from the sounds alone. Examples include Stephen Vitiello’sWorld Trade Center Recordings: Winds After Hurricane Floyd, Jana Winderen’sThe Noisiest Guys on the Planet, Jacob Kirkegaard’s4 Rooms, Christina Kubisch’s compositions based on observations of the Ruhr district, Anne Niemetz and Andrew Pelling’sThe Dark Side of the Cell(2004) as well as Andrea Polli’sHeat and Heartbeat of the City(2004) based on weather data from New York. The article asks how these compositions establish their relation to a specific context. How do they invite the listener to include his or her knowledge of specific contexts? The article suggests four relevant terms that are useful when studying this relation between text and context:paratext,intermediality,enunciationandmediality.

scholarly journals Learning-Based Predictive Building Energy Model Using Weather Forecasts for Optimal Control of Domestic Energy Systems

2018 ◽  
Vol 11 (1) ◽  
pp. 147 ◽  
Author(s):  
Byung-Ki Jeon ◽  
Eui-Jong Kim ◽  
Younggy Shin ◽  
Kyoung-Ho Lee
Keyword(s):  
Predictive Control ◽  
Prediction Models ◽  
Weather Forecasting ◽  
Reference Model ◽  
Weather Prediction ◽  
Building Energy ◽  
Energy Model ◽  
Weather Data ◽  
Rc Building ◽  
Mean Square Errors

The aim of this study is to develop a model that can accurately calculate building loads and demand for predictive control. Thus, the building energy model needs to be combined with weather prediction models operated by a model predictive controller to forecast indoor temperatures for specified rates of supplied energy. In this study, a resistance–capacitance (RC) building model is proposed where the parameters of the models are determined by learning. Particle swarm optimization is used as a learning scheme to search for the optimal parameters. Weather prediction models are proposed that use a limited amount of forecasting information fed by local meteorological centers. Assuming that weather forecasting was perfect, hourly outdoor temperatures were accurately predicted; meanwhile, differences were observed in the predicted solar irradiances values. In investigations to verify the proposed method, a seven-resistance, five-capacitance (7R5C) model was tested against a reference model in EnergyPlus using the predicted weather data. The root-mean-square errors of the 7R5C model in the prediction of indoor temperatures on all the specified days were within 0.5 °C when learning was performed using reference data obtained from the previous five days and weather prediction was included. This level of deviation in predictive control is acceptable considering the magnitudes of the loads and demand of the tested building.

scholarly journals Flattening the Electricity Demand Profile of Office Buildings for Future-Proof Smart Grids

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2357
Author(s):  
Rick Cox ◽  
Shalika Walker ◽  
Joep van der Velden ◽  
Phuong Nguyen ◽  
Wim Zeiler
Keyword(s):  
Smart Grids ◽  
Energy Demand ◽  
Prediction Models ◽  
Storage System ◽  
Demand Forecasting ◽  
Building Energy ◽  
Electricity Demand ◽  
Electricity Load ◽  
Load Shape ◽  
Demand Profile

The built environment has the potential to contribute to maintaining a reliable grid at the demand side by offering flexibility services to a future Smart Grid. In this study, an office building is used to demonstrate forecast-driven building energy flexibility by operating a Battery Electric Storage System (BESS). The objective of this study is, therefore, to stabilize/flatten a building energy demand profile with the operation of a BESS. First, electricity demand forecasting models are developed and assessed for each individual load group of the building based on their characteristics. For each load group, the prediction models show Coefficient of Variation of the Root Mean Square Error (CVRMSE) values below 30%, which indicates that the prediction models are suitable for use in engineering applications. An operational strategy is developed aiming at meeting the flattened electricity load shape objective. Both the simulation and experimental results show that the flattened load shape objective can be met more than 95% of the time for the evaluation period without compromising the thermal comfort of users. Accurate energy demand forecasting is shown to be pivotal for meeting load shape objectives.

Simulation of New Solar Heating System for Heating Biogas Digesters above Ground in Cold Regions

2013 ◽  
Vol 291-294 ◽  
pp. 89-95 ◽  
Author(s):  
A.A.M. Hassanein ◽  
Ling Qiu
Keyword(s):  
Solar Energy ◽  
Biogas Production ◽  
Heating System ◽  
Building Energy ◽  
Simulation Software ◽  
Solar Heating ◽  
Cold Weather ◽  
Weather Data ◽  
Building Energy Simulation ◽  
Energy Plus

The biogas amounts with stable flowing rate require heating in cold weather. This study focuses on using solar energy for heating biogas digester. In this research we used energy plus building energy simulation software and real weather data for simulation the heating of biogas digester with 8760 hours simulation .The research was carried out in two parts: The first one is one biogas digester above ground without heating. The Second part of this study is a simulation of one biogas digester with solar heating by using a new design based on double plastic cover. It has shown that the use of solar energy can achieve the optimum temperature for biogas production process almost the year time. Using double plastic cover is the most suitable method with economic form for heating biogas digester above ground.

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.

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