Abstract
Projecting the long-term trends in energy demand is an increasingly complex endeavor due to the uncertain emerging changes in factors such as climate and policy. The existing energy-economy paradigms used to characterize the long-term trends in the energy sector do not adequately account for climate variability and change. In this paper, we propose a multi-paradigm framework for estimating the climate sensitivity of end-use energy demand that can easily be integrated with the existing energy-economy models. To illustrate the applicability of our proposed framework, we used the energy demand and climate data in the state of Indiana to train a Bayesian predictive model. We then leveraged the end-use demand trends as well as downscaled future climate scenarios to generate probabilistic estimates of the future end-use demand for space cooling, space heating and water heating, at the individual household and building level, in the residential and commercial sectors. Our results indicated that the residential load is much more sensitive to climate variability and change than the commercial load. Moreover, since the largest fraction of the residential energy demand in Indiana is attributed to heating, future warming scenarios could lead to reduced end-use demand due to lower space heating and water heating needs. In the commercial sector, the overall energy demand is expected to increase under the future warming scenarios. This is because the increased cooling load during hotter summer months will likely outpace the reduced heating load during the more temperate winter months.
Highlights
The U.S energy infrastructure is capital intensive and requires significant investments in the planning and operation of the systems to ensure supply adequacy under a range of future contingencies
A multi-paradigm framework to assess the impacts of climate change on end-use energy demand data, and ‘projection’ for longer–term forecasts that are contingent on scenarios used in the model
Energy consumptions in the residential and commercial sectors are driven by complex interactions between socio-economic conditions, available technologies, land-use patterns, A multi-paradigm framework to assess the impacts of climate change on end-use energy demand characteristics of the built environment, policy landscapes and climatic conditions of a given region
Summary
The U.S energy infrastructure is capital intensive and requires significant investments in the planning and operation of the systems to ensure supply adequacy under a range of future contingencies. There exist many sophisticated energy-economy models for projecting medium- and long-term trends in the energy infrastructure such as MARKAL (MARKet ALlocation) [4], TIMES (The Integrated MARKAL-EFOM System) [5] and NEMS (National Energy Modeling System) [6] While these models can account for factors such as the future changes in technology, socio-economic conditions, and policy impacts, they are not able to adequately incorporate uncertainties associated with shifts in end-use energy demand due to climate variability and change [3,7]. Steemers and Yun [18] developed a Generalized Linear Model (GLM)—using the cross-sectional Residential Energy Consumption Surveys (RECS) data in 2001—to examine the interactions between occupants’ behavior, building systems and climatic characteristics Their objective was to examine the roles of occupant behavior ( in terms of space conditioning) and socio-economic factors in shaping energy demand curves. Metropolis-Hastings algorithm is typically used to characterize the posterior probability space [42,43]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
