Buildings contribute to greenhouse gas emissions that cause environmental impacts on climate change. Net Zero Energy (NZ) buildings would reduce greenhouse gases. The current definition of NZ lacks consensus and has created uncertainties, which cause delays in the adoption of NZ. This paper proposes a Process for Clarification to Accelerate the Net Zero (PC-A-NZ) through three integrated steps: variations, strategies, and requirements. We expand on the results in published NZ literature to clarify the differences in definition and strategy. The objective of this review is to (1) distinguish current variable parameters that are slowing the acceptance of NZ, and (2) focus the discussion internationally on moving faster toward applying NZ to a larger common agreement. The publications of global NZ target assessment and energy efficient strategies will be reviewed to address the main requirements in expediting NZ’s successful progress. Our NZ review analysis highlights (1) how the existing NZ definitions and criteria differ, (2) how calculation strategies vary, and (3) how standards and requirements are often localized. The proposed PC-A-NZ will help policymakers and stakeholders to re-evaluate the existing definitions, standards, and requirements to optimize the use of renewable technologies, improved energy efficiency and electrification to speed up achieving the NZ targets. Definition: There are multiple NZ definitions that vary in source and supply requirement, timescale, emission source, and grid connection.

Energy consumption in buildings is a significant cost to the building’s operation. As faults are introduced to the system, building energy consumption may increase and may cause a loss in occupant productivity due to poor thermal comfort. Research towards automated fault detection and diagnostics has accelerated in recent history. Rule-based methods have been developed for decades to great success, but recent advances in computing power have opened new doors for more complex processing techniques which could be used for more accurate results. Popular machine learning algorithms may often be applied in both unsupervised and supervised contexts, for both classification and regression outputs. Significant research has been performed in all permutations of these divisions using algorithms such as support vector machines, neural networks, Bayesian networks, and a variety of clustering techniques. An evaluation of the remaining obstacles towards widespread adoption of these algorithms, in both commercial and scientific domains, is made. Resolutions for these obstacles are proposed and discussed.

A Net Zero Community (NZC) concept and its energy characteristics are presented in this paper. NZC is an emerging topic with multiple variations in terms of scope and calculated methods, which complicates quantifying its performance. This paper covers three key barriers in achieving NZC targets: (1) the main focus of current definitions on buildings, disregarding community power systems and energy use in transportation; (2) different requirements (source, supply, metrics, etc.) in the existing definitions; and (3) lack of updated published reports to track the progress of committed NZC targets. The importance of this research is summarized as due to increased savings in primary energy and greenhouse gas emissions related to the three main energy sectors, namely power systems, buildings, and transportation (PBT). To clarify the current NZC, this paper reviews: (1) variations in the existing definitions and criteria from peer-reviewed publications; (2) the latest climate projection models by policymakers to achieve net zero by 2050; (3) the literature on renewable-based power systems; and (4) three planned NZC cases in international locations, in order to study their NZC targets, energy performance, and challenges. The outcome highlights NZC design guidelines, including energy efficiency measures, electrification, and renewables in PBT sectors that help stakeholders including policymakers, developers, designers, and engineers speed up achievement of NZC targets.

Automated fault detection and diagnostics in building systems using machine learning (ML) can be applied to commercial buildings and can result in increased efficiency and savings. Using ML for FDD brings the benefit of advancing the analytics of a building. An automated process was developed to provide ML-based building analytics to building engineers and operators with minimal training. The process can be applied to buildings with a variety of configurations, which saves time and manual effort in a fault analysis. Classification analysis is used for fault detection and diagnostics. An ML analysis is defined which introduces advanced diagnostics with metrics to quantify a fault’s impact in the system and rank detected faults in order of impact severity. Explanations of the methodology used for the ML analysis include a description of the algorithms used. The analysis was applied to a building on the Texas A&M University campus where the results are shown to illustrate the performance of the process using measured data from a building.