A fresh look at weather impact on peak electricity demand and energy use of buildings using 30-year actual weather data
Introduction
Buildings consume more than one third of the world’s total primary energy. The IEA Annex 53 [1] identified and studied six influencing factors on building energy performance, including climate, building envelope, building equipment, operation and maintenance, occupant behavior, and indoor environmental conditions. Among these influencing factors, climate plays a unique and significant role. Weather contributes directly and significantly to the variations of thermal loads and energy use of HVAC (heating, ventilation, and air conditioning) systems, lighting (for buildings with daylighting controls), and energy production from solar-based renewable systems. In residential and commercial buildings in the US, heating and cooling accounts for more than 40% of end-use energy demand. It is important to understand and estimate the impact of weather on the long-term performance of buildings in order to support policy making, and to allow building operators and owners to respond better to climate changes in terms of building energy supply and demand. Additionally, considering the impact of yearly variations in weather can improve the evaluation of investment risks of energy conservation measures (ECMs) for new and existing buildings by taking into account their life-cycle energy and cost savings.
The accuracy of building energy simulations and economic assessments of renewable energy systems depend on the availability of reliable weather data. There are two primary sources of weather data that are used to generate weather data files used in building simulation: measured weather data using physical sensors and observations, and simulated data using mathematical weather models. Various methods to generate annual hourly weather data have been developed in the past. Such weather data include the Typical Meteorological Year (TMY), the test reference year (TRY), the weather year for energy calculation (WYEC), the design reference year (DRY), as well as the synthetically modeled meteorological year (SMY). However, the lack of long-term weather records usually limits the generation of typical annual weather data files in any format [2].
A TMY weather file contains hourly values of solar radiation and meteorological elements for a 1-year period. The 12 typical meteorological months (TMMs) are selected from various calendar months in a multi-year weather database. The criteria for TMM selection is based on the statistical analysis and evaluation of four weather parameters: the ambient dry-bulb temperature, the dew-point temperature, the wind speed and the global solar radiation. Algorithms are used to smooth discontinuities from the data to avoid drastic changes between two adjacent months selected from different years. The first generation of TMY weather data for the US is derived from the 1952–1975 SOLMET/ERSATZ database, while the second generation of data (TMY2) is derived from the 1961–1990 National Solar Radiation Database (NSRDB) covering 239 US locations. The latest, third generation data (TMY3) is derived from the 1976–1990 and 1991–2005 National Solar Radiation Data Base (NSRDB). TMY3 covers 1020 US locations. TMY, TMY2 and TMY3 data sets cannot be used interchangeably because of differences in the data structure such as time (solar vs. local), formats, elements, and units. The intended use of TMY weather data is for computer-based building performance simulations of solar energy conversion systems and building systems to facilitate performance comparisons of different system types, configurations, and locations in the US and its territories. Because they represent typical rather than extreme conditions, they are not suited for designing systems to meet the worst-case conditions occurring at a location [3]. For the calculations of peak cooling and heating loads of buildings, and sizing HVAC equipment, design day weather data are used. Design-day weather data tend to represent more extreme weather conditions in order to guarantee that HVAC systems can meet peak loads for most of the time during their life cycle. Various methods are used to create design-day weather data [4].
As TMY data may not be available for some cities or sites, SMY weather data provide a practical and useful alternative. SMY weather data can be generated from monthly average or total values of weather parameters using stochastic models and auto-regressive moving average processes to represent the seasonal and daily weather variations [5]. Such stochastic weather models can be used to generate AMY weather data for use in deterministic building simulations, or together with a stochastic internal loads model, can be integrated with a building thermal model to obtain directly the probability distribution of building performance to investigate the uncertainty caused by the random meteorological processes and internal heat gains [6].
A new online weather data service with immediate access to precision, localized weather history, current conditions and forecasts are presented by Keller and Khuen [7]. Localized weather data is created by integrating all available ground station observations with high-resolution datasets from NOAA (National Oceanic and Atmospheric Administration). Both historical and forecast time series data are available for direct user access and application/system access through Web Data Services and API interfaces.
Selecting appropriate weather data to be used in building performance simulation is important. The use of inappropriate weather data can result in large discrepancies between the predicted and measured performance of buildings. In the late 1970s, Freeman [8] evaluated how well TMY represents actual long-term weather data based on simulations of an active residential space solar heating and cooling system for six US climates, Albuquerque, Fort Worth, Madison, Miami, New York, and Washington DC. High variability of the weather and solar heating system performance year to year was noted. Crawley [9] compared the influence of the various weather data sets on simulated annual energy use and cost. Using different weather data sets can cause significant variations in annual energy consumption and cost from simulation results. The results show that the TMY and the WYEC data sets represent the closest typical weather patterns. Simulated results using the TMY weather data provides the average/typical energy use for buildings, but the peak electricity demand predictions and uncertainty analyses based on TMY are often not reliable because a single year cannot capture the full variability of the long-term climate change [10]. In view of the long-term climate change, the time period assigned for TMY selection should include the most recent meteorological data and should be reasonably long to reflect well the weather variations [11]. Most of the available TMY weather data are from weather stations located at airports. It is possible to create a new TMY file localized to a building location by integrating the weather station observations with gridded reanalysis data. However, there are limited complete weather data collected by weather stations over 15–30 years, so TMY data is only available for only 1020 locations. Furthermore, some of the TMY weather data files were created up to 20 years ago. They are less representative of the typical present day climate and do not describe extreme weather conditions. Compared with the TMY weather data, the AMY is created from actual hourly data for a particular calendar year. AMY weather data is particularly useful for modeling years with extremes in weather and verifying the energy performance of buildings. However, as with the TMY weather data, the AMY weather data needs to be chosen as close to the building location as possible.
The potential impacts of various types of weather forecast models, weather data, and building prototypes have been studied from a number of perspectives. A prototypical small office building was modeled operating at three energy efficiency levels, using typical and extreme meteorological weather data for 25 locations, to study various predicted climate change and heat island scenarios [12]. The largest change to the annual energy use due to climate change was seen in the temperate, mid-latitude climates, where there was a swapping of energy use from heating to cooling. The heating energy was reduced by more than 25% and cooling energy was increased by up to 15%. The TMY weather data provides more localized and comprehensive climate indicators to further support the HVAC system design in buildings [3], [13]. The space cooling plays a major role in determining the magnitude and timing of peak electricity demand. The archived General Circulation Model (GCM) projections were statistically downscaled to the site scale, which were then used for input to building cooling and heating simulations to study the California specific impact of global warming on building energy consumption [14]. The IPCC’s different carbon emission scenarios predict that climate change will lead to a 25–50% increase in space cooling electricity use over the next 100 years. Under the worst case carbon emission scenario the total energy consumption will increase between 8% and 20%. The energy performance of an office building in Hong Kong, using multi-year weather data sets was simulated to investigate the diversity in simulation predictions [15]. The results concluded that the choice of weather data sets was not crucial for the comparative energy studies during the initial design stage. However, it becomes important to select a particular standard weather year data set when absolute energy consumption data are required. Similar studies on office buildings were conducted in five major climate zones in China by using multi-year weather databases as well as TMY data [16], [17], [18]. The results showed a decreasing trend for heating loads and an increasing trend for cooling loads due to predicted climate change. The monthly loads and energy use profiles calculated using the TMY and long-term means profiles fell well within the maximum and minimum ranges of the 30-year individual predictions. It was concluded that building performance predictions using TMY weather data can be used in comparative energy efficiency studies.
In recent years, various types of weather data have been used in building simulation to evaluate energy performance and demand response. Accurate estimation of building performance relies on the appropriate selection of accurate weather data. The quality of weather data and their impact on building cooling and heating loads and energy consumption were studied by comparing three weather datasets for a specific location for the calendar year 2010 [19]. The three sources of data included site measured data and AMY weather data provided by two vendors. Key weather variables from the three datasets were compared statistically, and building loads and energy use were simulated using EnergyPlus version 6.0. The study concluded that the maximum difference in individual hourly weather variables can be as high as 90%, annual building energy consumption can vary by ±7%, while monthly building loads can vary by ±40% when using different weather datasets.
Using TMY weather data to calculate the energy use in buildings aims to represent the average or typical values. However, different types of buildings with different energy service systems and operation strategies have different responses to weather. Furthermore, a single set of energy use results from TMY simulations does not provide the range of variations due to the change of weather from year to year. The typical life of a building is more than 50 years; therefore the assessment of long-term building performance becomes very important. TMYs are often recommended to be used in building simulations to evaluate and compare performance of design alternatives under the assumption that energy savings from a design alternative would not vary noticeably with yearly weather variations. This assumption is not necessarily true. Although previous studies have demonstrated actual weather has a significant impact on peak electric demand and energy use in buildings, there are limited studies that focus on investigating the sensitivity of energy savings and peak demand reduction of energy conservation measures to the yearly variation of weather, using multi-decade AMY weather data across a complete coverage of climate zones for typical commercial buildings. This study aims to address that gap in the literature.
This study does not touch the topics of previous studies on impacts of long-term climate change or local heat island effects on building performance; instead it focuses on providing insights to the following important questions:
- (1)
How significant is the weather impact on both the peak electricity demand and building energy use?
- (2)
Does the simulated building energy use using the TMY3 weather data represent the average or typical energy use over a 30-year period?
- (3)
Building simulation results from which climates are greater affected by using different weather data sets?
- (4)
What types of office buildings are subject to the greatest impact of weather?
- (5)
What are the risks from using the TMY3 weather data in building simulations to evaluate the energy savings and electricity demand reduction of energy efficiency technologies?
Through better understanding of which building technologies and system designs are more sensitive to yearly weather variation, building designers, owners, operators, and policy makers can make more informed decisions on energy efficiency implementations to reduce peak electricity demand and building energy use.
Section snippets
Overview
To study the impact of weather on building performance, the most typical commercial buildings located in typical climate zones are the natural starting point. The US 2003 Commercial Building Energy Consumption Survey (CBECS) [20] indicates that office buildings are the most common building type, comprising the largest floor area, and consuming the most energy in the commercial building sector. Therefore, the prototypical office buildings with three different sizes at two design efficiency
Variations of weather data
Variations of weather data and climate zone classification for each of the 17 cities based on the annual HDD18 (Heating Degree Days with base temperature of 18 °C) and CDD10 (Cooling Degree Days with base temperature of 10 °C) of the AMY data from 1980 to 2009 are illustrated in Fig. 2. The climate zones displayed in Fig. 2 correspond to the criteria listed in Table 1. It can be seen that most cities do not belong to only one climate zone. For the 30-year period, the climates of some cities vary
Conclusions
Nowadays with the availability of long-term AMY weather data and sufficient computational power of personal computers, it is feasible and necessary to run simulations with AMY weather data covering multiple decades to fully assess the impact of weather on the long-term performance of buildings, and to evaluate the energy savings potential of energy conservation measures for new and existing buildings from a life cycle perspective. Main findings from this study are: (1) annual weather variation
Acknowledgements
This work was supported by the US Department of Energy under the US–China Clean Energy Research Center on Building Energy Efficiency. It is also part of our research activities for the IEA ECBCS Annex 53: Total Energy Use in Buildings – Evaluation and Analysis Methods. This work was co-sponsored by the Bureau of Energy, Ministry of Economic Affairs, Taiwan, ROC.
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