Digitalization is a driver of decarbonization. However, the fundamental question remains: what are the Key Performance Indicators (KPIs) required to measure the impact of decarbonization (Scope 1, 2 and 3)?

Digitalization is a key component of the Singapore Green Plan 2030 strategy, giving customers more control and enabling a variety of applications and business models, such as digital twins, design for maintainability, AI-driven intelligence, demand flow, energy optimization, resilience with microgrids, and virtual power plants, to reduce carbon emissions without incurring additional costs. Decarbonization policies and technological advancements are already promoting the emergence of a new energy system. Grid edge is enabling more consumers to become prosumers by replacing large-scale, centralized power generation with a network of small-scale, decentralized, low-carbon generation assets.

While investors are applying environmental, social, and governance (ESG) pressure to buildings and industrial firms, there is no way to avoid the issue of decarbonization. The transition to a net-zero carbon economy, as required by the 2015 Paris Agreement, will continue to have a positive impact on economies and societies around the world.

Companies embarking on their unique decarbonization journeys are clearly aware that any fundamental change comes with possibilities and risks. For example, increased OPEX expenditures owing to rising energy prices or unforeseen regulatory changes could pose a risk. A firm commitment to decarbonization, on the other hand, presents a priceless opportunity for future generations.

Here are detailed considerations to guide building and industrial customers on KPIs for a successful decarbonization journey.

Quantifying building energy performance through the creation and use of Key Performance Indicators (KPIs) is a critical step in achieving energy-saving goals in both new and existing infrastructures. However, the current methods for evaluating improvements are not well represented at the system level (e.g., lighting, plug-loads, HVAC, service water heating). Instead, they are usually only measured at the aggregated equipment level (e.g., energy use intensity) or at the equipment level (e.g., chiller efficiency coefficient of performance (kW/RT)) with limited insights for benchmarking and diagnosing deviations in performance of aggregated equipment that provides a specific service to a building (e.g., cooling, lighting).

Through improved data collection, the increasing installation of sensors and meters in buildings makes evaluating building performance at the system level more feasible. This study takes advantage of this opportunity by introducing a set of system-level KPIs that cover four major building end-use systems: lighting, MELs (Miscellaneous Electric Loads, plug loads, EV loads), and HVAC and SWH (service water heating), as well as other subsystems that make them up.

Energy use, peak demand, load shape, occupant thermal and visual comfort, ventilation, and water use are among the system KPIs that have been reformulated in a new context to represent various types of performance.

System KPIs could be used for system performance benchmarking and diagnostics, code compliance, and measurement and verification, among other things.

Buildings can use less energy if energy efficiency measures are implemented (for example, installing new energy efficient technologies like Demand Flow® and fine-tuning temperature set-points through improved control logic). In fact, such simple measures have demonstrated energy savings of around 20% without compromising building services or occupant comfort.

With emerging technologies such as digital twin based closed loop Demand flow®, an average of 36% (with a range of 23% to 60%) energy savings could be achieved in commercial & mixed-use buildings. These improvements are over and above designed product efficiency, Thanks to digital twin embarking on emerging technologies like data lakes, ML and AI. Siemens is instrumental in improving the EUI up to 16% for SLEB (Super Low Energy Building) without the need of replacing existing capital equipments.

Building performance in terms of energy use must be quantified as a baseline for assessing potential savings as well as evaluating and validating improvements. This information is useful during the planning, design, construction, and commissioning phases of new constructions.

Quantifying baseline energy performance is necessary when performing fault detection and diagnostics (FDD), retro-commissioning, M&V, and making retrofit decisions during the operations and maintenance phase of buildings. However, due to the complexity of the building stock (e.g., building type, climate zones, vintage), configurations (e.g., types of building services, system operations, control strategies), and stochastic variables, accurately assessing baseline energy performance in buildings is difficult (e.g., weather conditions, occupant actions).

Building Performance Indicators levels

Siemens approaches to ZEB/SLEB ( Zero Energy Buildings /Super Low Energy Buildings is in two sections:

1. Total Energy Management

2. Integrated digitally driven Smart facility management

Some examples of various levels building performance indicators, Let us start with whole building level performance indicators, and Siemens technologies’ potential to improve.

*Building-level performance indicators toward Super low energy buildings*
KPI and units	Definition	Example	Siemens technology	*Individual system estimated improvement potential up to**
Energy Use Intensity EUI (Kwh/M2/year)	A building’s energy use normalized by its size (usually the total Gross floor area). EUI score comparing target building with similar buildings nationwide normalized by business activities (the bigger the better)	Simple benchmarking of building energy consumption, BCA Benchmarking for SLEB (Super Low Energy Building)	Demand Flow® Demand Control Ventilation and Indoor Air Quality™	16%
Energy Performance Coefficient (kWp)	performance indicator considering the yearly consumption and on-site renewable power generation	Assess the impact of modifications of building on energy consumption. Guide new building design.	SINACON PV™	30%
Lifecycle building energy use (TCO)	Total site energy use of a building in its whole life cycle, total cost of ownership	Assess building design or energy efficiency technologies and their impact on a building’s whole life cycle.	Digital Twin	23%-60%
Electrical Load Factor	The average electrical load divided by the peak load in a specified time period.	building system design, control, and operation	NXPLUS C™ SIRIUS™	10%
Zero Energy Performance Index	A rating score indicating how likely a building is to be net zero (the smaller the closer to net zero)	NBI zero building performance evaluation	Total Energy Management	80%
Whole Building Performance Indicator	An aggregated performance indicator considering lighting, thermal comfort, maintenance, and indoor air quality	A web-based building assessment toolkit	Navigator®	30%
System-level performance indicators
Daylight Effectiveness Indicator	A metric that reflects monthly lighting energy use density considering daylit hours	Evaluate the lighting and daylight effectiveness in existing buildings	Enlighted™	72%
Evaluate the lighting and daylight effectiveness in existing buildings (RTh/$)	A ratio of the sum of a building’s annual cooling load in thousands of RTh to the sum of the annual cost in dollars of energy	Implementation of the metric in Asset Score tool.	Demand Flow®	50%
HVAC Operational Consistency Indicator	A metric reflecting the HVAC operation effectiveness under varying weather conditions.	Help to evaluate and benchmark the HVAC system operation and control strategies under certain weather conditions	Digital Twin Demand Flow®	23-60%
Chiller Plant Performance (kW/RT)	The ratio of real total cooling systems loads to the total system energy consumption to meet the loads.	cooling plant system efficiency	Demand Flow®	50%
Airside Performance (kW/RT)	The ratio of real total cooling systems loads to the total system energy consumption by Airside equipment (AHU/FCU etc.)	Airside system efficiency	Demand Flow®	50%
Plug-load Off-hours Ratio	The ratio of plug-load energy consumption during off-hours to the total plug-load consumption	Help to identify idle plug-load energy consumption.	Navigator® SENTRON PAC™	30%
EV Charging Performance (Charging rate)	Charging rate to the current carrying capacity	Help to identify when EV charger shall be free to use	SICHARGE™	20%
Equipment-level performance indicators
Chiller (kW/RT) CHWP (kW/RT) CWP (kW/RT) CT (kW/RT)	The ratio of real total cooling systems loads to the total system energy consumption by Chillers	Building energy simulations. Design and sizing of building system. Equipment level FDD (Fault detection and diagnostics). Chiller efficiency CHWP efficiency CWP efficiency Cooling Tower efficiency	Demand Flow® SIMOTICS™ SINAMICS™ G120	50%
Luminous Efficacy (lm/W)	The ratio of luminous flux to the electric power, measuring of how well a light source produces visible light	selection of lighting fixtures.	Enlighted™	10%
IAQ / IEQ parameters	Health and wellbeing parameters	Selection of TFA / MOU and building envelope	Fine Dust Sensors and NBPI	15%
Sensors-level performance indicators
Accuracy / Deviation; Self checks	Total indication of Mean deviation and allowable range (SS 591 for Chiller plant)	FDD for sensor level for Heat balance, accuracy, and comfort	Navigator®	30%
Responsiveness to Control	pinpoint control issues at equipment level	FDD for PID response	Navigator®	30%
Responsiveness to Service Demand/ Calibration	Indicate the calibration and service history	FDD for Calibration and accuracy	Navigator®	30%

The challenge of data availability and data quality issues in new and existing buildings along with large concerns like cybersecurity bring the importance of trusted brand like Siemens on the forefront of opportunities, as well as the potential applications drivers of system KPIs. System KPIs from the simulation database, such as major end use intensity, peak demand, and load profile, demonstrate their utility for system level performance benchmarking, diagnostics, code compliance, and M&V.

To enhance the potential adoption of these various system KPIs as a new system performance compliance path for building energy codes and standards, it is important to reach out to the BCA Green Mark Scheme, Singapore Green Building Council (SGBC) and ASHRAE community.