Planning for net zero by 2050, what HVAC system interventions will today's code minimum commercial buildings require?
Heating, Ventilation and Air conditioning (HVAC) systems account for approximately 40% of the total energy used by buildings in the USA. To reduce this consumption States enforce minimum energy codes that currently range from strict (ASHRAE 90.1-2016) to relaxed (ASHRAE 90.1-2007) with some states following no particular standard. To reach as close as possible to net zero carbon and energy, each statewide energy code requires different levels of interventions for each code minimum building. This paper presents a collection of potential HVAC retrofits to transition each State's current code minimum buildings towards the goal of net zero to achieve a carbon free future by 2050. The study shall use a large array of code minimum criteria and climate zones covering the 48 contiguous United States to determine the most successful interventions at reducing the energy use of buildings meeting today's energy codes. Office use type has been selected for the study as they account for 18% of total buildings and close to 19% of the total commercial floorspace. A number of interventions will be applied to this use type that will vary based on current code and climate zone; however, a common theme will be electrification through the use of heat pump technology. Each intervention will be scored based on energy and carbon savings, along with level of difficulty and cost. The study not only provides a comprehensive transition guideline toward the net zero energy and carbon but also predicts the future project opportunities.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Hybrid AC/DC Transmission Expansion Planning Considering HVAC to HVDC Conversion Under Renewable Penetration
In this paper, a dynamic (i.e. multi-year) hybrid model is presented for Transmission Expansion Planning (TEP) utilizing the High Voltage Alternating Current (HVAC) and multiterminal Voltage Sourced Converter (VSC)-based…
LLM-based Question-Answer Framework for Sensor-driven HVAC System Interaction
Question-answering (QA) interfaces powered by large language models (LLMs) present a promising direction for improving interactivity with HVAC system insights, particularly for non-expert users. However, enabling accurat…
Response GenerationOn the role of planning in model-based deep reinforcement learning
Model-based planning is often thought to be necessary for deep, careful reasoning and generalization in artificial agents. While recent successes of model-based reinforcement learning (MBRL) with deep function approximat…
Deep Reinforcement LearningModel-based Reinforcement Learningreinforcement-learningReinforcement Learning+1What if? Causal Machine Learning in Supply Chain Risk Management
The penultimate goal for developing machine learning models in supply chain management is to make optimal interventions. However, most machine learning models identify correlations in data rather than inferring causation…
Decision MakingManagementHVAC-DPT: A Decision Pretrained Transformer for HVAC Control
Building operations consume approximately 40% of global energy, with Heating, Ventilation, and Air Conditioning (HVAC) systems responsible for up to 50% of this consumption. As HVAC energy demands are expected to rise, o…
In-Context Reinforcement LearningReinforcement Learning (RL)