Title

Energy Forecasting to Benchmark for Federal Net-zero Objectives under Climate Uncertainty

Document Type

Article

Publication Date

10-2022

Abstract

Climate variability creates energy demand uncertainty and complicates long-term asset management and budget planning. Without understanding future energy demand trends related to intensification of climate, changes to energy consumption could result in budget escalation. Energy demand trends can inform campus infrastructure repair and modernization plans, effective energy use reduction policies, or renewable energy resource implementation decisions, all of which are targeted at mitigating energy cost escalation and variability. To make these long-term management decisions, energy managers require unbiased and accurate energy use forecasts. This research uses a statistical, model-based forecast framework, calibrated retrospectively with open-source climate data, and run in a forecast mode with CMIP5 projections of temperature for RCPs 4.5 and 8.5 to predict total daily energy consumption and costs for a campus-sized community (population: 30,000) through the end of the century. The case study of Wright-Patterson AFB is contextualized within the existing Executive Orders directing net-zero emissions and carbon-free electricity benchmarks for the federal government. The model suggests that median annual campus electric consumption, based on temperature rise alone, could increase by 4.8% with RCP4.5 and 19.3% with RCP8.5 by the end of the century, with a current carbon footprint of 547 million kg CO2e. Monthly forecasts indicate that summer month energy consumption could significantly increase within the first decade (2020-2030), and nearly all months will experience significant increases by the end of the century. Therefore, careful planning is needed to meet net-zero emissions targets with significant increases in electricity demands under current conditions. Policies and projects to reduce the carbon footprint of federal agencies need to incorporate forecasting models to understand changes in demand to appropriately size electric infrastructure.

Comments

© 2022 The Authors. Published by IOP Publishing Ltd.

The Version of Record of this article is going to be published on a gold open access basis under a CC BY 3.0 license. This Accepted Manuscript is available for reuse under a CC BY 3.0 license immediately.

DOI

10.1088/2634-4505/ac9712

Source Publication

Environmental Research: Infrastructure and Sustainability

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