Scaling Heating System Optimisation Through Stakeholder Collaboration

AI Quick Summary

Heating systems in private households emit 8’500kt CO2 every year. They could deliver better comfort, live longer, and consume less energy if they were optimised. Such optimisation requires continuous monitoring and analysis, and collaboration of multiple stakeholders. This project aims at delivering a proof-of-concept of how multiple benefits can be delivered simultaneously, how synergies lower the cost of technologies and set-up efforts, and how digitalisation and standardisation supports collaboration. [An extended project idea description is attached as PDF.]Project idea descriptionWhat problem would you like to solve?Heating systems in most buildings could deliver better comfort, live longer, and consume less energy if they were optimised. There is often a significant gap between the planned performance of a heating system and the actual performance in operation, which is known as the performance gap.Optimising a heating system is not trivial. Heating systems are complex systems that must adapt dynamically to a wide range of heat demand and are composed of several, more or less independently operating components. Understanding and optimising a complex system first and foremost requires continuous monitoring to record data about the operation of such a system under the whole range of operating conditions.Even if such data were available, most operating personnel is not skilled in drawing the right conclusions, which is known as the skills gap. The people who know how such a system should operate are the engineers who planned it. But they are expensive and, therefore, not available during the operating phase of a building. And even when they are available, then the effort it takes them to configure and to use current data analysis software restricts them to look at only a limited number of issues.The challenge for a solution to this problem is to create enough benefits for the end-customers to make it worth it, to keep the cost down by creating synergies, and to bring together all necessary skills. It would take the collaboration of people who are involved in the planning, installation, commissioning, and operation of buildings, and the development of low-cost monitoring and effective diagnostics software. This problem cannot be solved solely through the collaboration of just a solution provider and a customer. The goal of this proposed project is, therefore, to initiate such a collaboration through a proof-of-concept that should demonstrate the future roles and collaboration of these people, the required software and hardware, and the potential benefits.Heating systems are just one example of technical building systems. Cooling, ventilation, air conditioning, and, increasingly, flexible electrical energy systems show similar problems. Ever more flexible and interconnected energy systems that help to fight climate change also require ever more sophisticated monitoring and diagnostics capabilities.Who are the customers and how will they profit from a solution?There are multiple participants along the value chain who could directly benefit from this solution due to their current but also their possible future business models:CustomerBenefitBuilding owner-operators, e. g., buildings serving education, hospitality, or healthcareOptimised heating system operation leads to improved comfort, fewer breakdowns, longer lifetime, reduced energy consumption, lower operating costsNovel heating system service providers, e. g., Electrojoule in Switzerland or Green Fusion in GermanyLower operating costs when providing the service and increased benefits for their customers leads to higher margins and scalabilityExisting service providers with extended services, e. g., system integrators offering maintenance or planners offering continuous commissioningDigitalisation and technology-based process improvements open up new business opportunities that take advantage of prior investments into customer relationships and expertiseBuilding or district heating service providers, e. g., utilities in Switzerland or private providers abroadOptimised heating system operation leads to improved comfort, fewer breakdowns, longer lifetime, and reduced energy consumption, lower operating costsBesides these, indirect beneficiaries are:The tenants and users of the buildings through increased comfort,The investors of the buildings through more accurate assessment of the lifecycle costs of heating systems and better compliance with emissions regulations,The employees of the customers through upskilling and more responsibilities.How does your project idea affect energy savings or CO2 emissions?The goal of this project is to develop a solution that can be rolled out across the largest number of building types, in particular multi-family homes and smaller office buildings. Based on a portfolio of multi-family homes of our customer, we have seen during the Winter 22/23 that heating systems in multi-family homes can be optimized to achieve a 10-20% lower consumption. According to the SFoE, private households consume 120PJ annually of heating oil and gas for space heating and domestic hot water. This results in CO2 emissions of 8’500kt CO2 annually. Even a small relative improvement will result in a big absolute number.Additional effects that we have not quantified yet are less gray energy that is required to produce heating systems due to longer equipment lifetime and fewer maintenance truck roles due to fewer equipment breakdowns.Lastly, we have seen that we were able to reduce the return temperatures for all buildings with district heating. Lowering the return temperatures increases the efficiency and capacity of remote heat distribution networks. This will also result in lower CO2 emissions.Current status and previous activitiesWhat has been tried before?There are several current solutions that address either a limited set of customers or limited use cases. Either way, this puts such solutions out of reach for most buildings.There is the classic energy optimisation (energetische Betriebsoptimierung eBO) service that is offered by engineering companies. An engineer would then use a checklist or manually analyse available data to suggest optimisation procedures.There are software offerings that target technical monitoring and fault detection and diagnostics (FDD). Such software applies hundreds of rules to individual measurements or system components. The software is not cheap, requires potentially multiple months of set-up, and is meant for users that possess the expertise of system diagnostics.There are new software offerings that provide Machine Learning- or Artificial Intelligence-based energy optimisation. These typically provide more accurate demand forecasting than the originally installed building control system.Our own solution, the Plutinsus Visual Analytics solution, is currently used productively by a heating system maintenance provider at over 100 multi-family homes in Switzerland. Plutinsus Visual Analytics is an innovative software for the efficient operational diagnostics of heating systems. It provides currently 60 pre-defined types of analyses and has been designed to work on top of any IoT or monitoring system. These pre-defined analyses do not yet cover all the necessary cases.Plutinsus has an ongoing collaboration with James Allan to advance the use of diagnostics for the operation of the NEST building at Empa (https://linkedbuildingdata.net/ldac2023/abstracts.html#industry03) based on a digital twin using linked data. This digital twin will contain information about the physical composition of the technical building infrastructure of the NEST building.What was not successful?As described above, all these solutions work only in specific cases and are not broadly used due to limited benefits, high costs, or insufficient skills. Most of these solutions involve a single provider and target a single benefit.Building owners that rent out their space are often not motivated to implement measures that solely address energy efficiency because the energy costs are borne by the tenants.Current approaches for energy optimisation start with energy consumption measurement using certified equipment. In the case of heat consumption measurements, this requires flow meters that are expensive and costly to be installed into the piping.Solutions for technical monitoring are sometimes installed during commissioning and testing and then ripped out because they are too costly to operate and do not deliver enough benefits.In general, current solutions still require too much manual effort and technical skills to be provided at reasonable costs.Better solutions would require the collaboration of multiple providers. This goes against established business models. And currently everyone is making enough money and is not willing to invest in new solutions.The Plutinsus solution pre-defines analyses based on component types. This limits the efficiency of analysis for more complex systems without further explicit modelling of sub-systems and systems and of more complicated cause-and-effect relationships. This knowledge has been considered while planning the system but is not available to the diagnostics software.What have you learned?It is important that the solution offers sufficient benefits, that the costs are reasonable and that the necessary skills are available.The solution will have to address multiple benefits at once: assurance of quality of comfort, maximisation of equipment lifetime and predictability of maintenance and replacement costs, minimisation of energy consumption and greenhouse gas emissions, support for planning of retrofits.We must find synergies to keep the cost down of items that are unique to each site or building and cannot be reducing through scaling:Hardware to collect measurements should be shared among all interested parties by using open standards.Monitoring and diagnostic software must become smarter by incorporating knowledge about the functioning and the specific configuration of the technical building infrastructure to completement the skill and expertise of the users and to increase the efficiency and quality of the analysis.The required information about the specific building set-up that is required to configure such diagnosis system should be created during the planning and installation phases.The solution should facilitate the collaboration of stakeholders with different skill levels. There will always be issues that will require a highly trained engineer. It is crucial that such skills are available if needed.The business models of all involved parties should be kept as scalable as possible. It is particularly important to distinguish between product and service business models. This requires a seamless integration of the offerings of multiple solution providers.An optimised heating system can be achieved through continuous monitoring and commissioning using operational data. This result does not require expensive heat energy measurement. We should focus on results rather than methods. Resources neededWhat are your planned work packages?The goal of this project is to develop a proof-of-concept that demonstrates how this concept works and how it can be scaled using a representative demonstration site. It should showcase the activities and necessary skills of all involved people, the novel value propositions and business models for the various stakeholders, and the required hardware and software.Kick off the project.Define project scope.Pick a representative demonstration site and set of use cases.Develop answers to the following questions.What is the information needed in order to perform the tasks of the use cases?Who has got the required skills to perform these tasks and how could various people collaborate?What does the software need to know such that it can calculate the performance metrics, present the information to the user, and guide the analysis?Who can provide this information?Apply method to chosen demonstration site.Conduct and evaluate the test at the demonstration site.Conclude and disseminate the findings.How can the Energy Lab help you?The proposed project requires the collaboration of multiple stakeholders along the value chain. Such coordination is hard to achieve through the initiative of any stakeholder by themselves. We hope that we can find respective partners who plan, install, commission, and operate heating systems, as well as academic partners who can close the gap between what is needed and what is state-of-the-art through the network of the Energy Lab.Prof. Dr. Olivier Steiger (HSLU Institute for Building Technology and Energy IGE) and Dr. James Allan (Empa Urban Energy Systems Laboratory and NEST building demonstrator) have – tentatively and non-bindingly – offered to be a project partner.We will use the CHF 20’000 price money to motivate these partners to collaborate with us by covering (some of) their expenses. We foresee that this money is mainly used for the academic partners and for the set-up of the demonstration site.

Project Idea Metadata

Project Idea Name: Scaling Heating System Optimisation Through Stakeholder Collaboration
Date: 8/16/2023 2:22:55 PM
Administrators:
- Wolfram Willuhn

Project Idea Description

[An extended project idea description is attached as PDF.]

Project idea description

What problem would you like to solve?

Heating systems in most buildings could deliver better comfort, live longer, and consume less energy if they were optimised. There is often a significant gap between the planned performance of a heating system and the actual performance in operation, which is known as the performance gap.

Optimising a heating system is not trivial. Heating systems are complex systems that must adapt dynamically to a wide range of heat demand and are composed of several, more or less independently operating components. Understanding and optimising a complex system first and foremost requires continuous monitoring to record data about the operation of such a system under the whole range of operating conditions.

Even if such data were available, most operating personnel is not skilled in drawing the right conclusions, which is known as the skills gap. The people who know how such a system should operate are the engineers who planned it. But they are expensive and, therefore, not available during the operating phase of a building. And even when they are available, then the effort it takes them to configure and to use current data analysis software restricts them to look at only a limited number of issues.

The challenge for a solution to this problem is to create enough benefits for the end-customers to make it worth it, to keep the cost down by creating synergies, and to bring together all necessary skills. It would take the collaboration of people who are involved in the planning, installation, commissioning, and operation of buildings, and the development of low-cost monitoring and effective diagnostics software. This problem cannot be solved solely through the collaboration of just a solution provider and a customer. The goal of this proposed project is, therefore, to initiate such a collaboration through a proof-of-concept that should demonstrate the future roles and collaboration of these people, the required software and hardware, and the potential benefits.

Heating systems are just one example of technical building systems. Cooling, ventilation, air conditioning, and, increasingly, flexible electrical energy systems show similar problems. Ever more flexible and interconnected energy systems that help to fight climate change also require ever more sophisticated monitoring and diagnostics capabilities.

Who are the customers and how will they profit from a solution?

There are multiple participants along the value chain who could directly benefit from this solution due to their current but also their possible future business models:

Customer

Benefit

Building owner-operators, e. g., buildings serving education, hospitality, or healthcare
Optimised heating system operation leads to improved comfort, fewer breakdowns, longer lifetime, reduced energy consumption, lower operating costs
Novel heating system service providers, e. g., Electrojoule in Switzerland or Green Fusion in Germany
Lower operating costs when providing the service and increased benefits for their customers leads to higher margins and scalability
Existing service providers with extended services, e. g., system integrators offering maintenance or planners offering continuous commissioning
Digitalisation and technology-based process improvements open up new business opportunities that take advantage of prior investments into customer relationships and expertise
Building or district heating service providers, e. g., utilities in Switzerland or private providers abroad
Optimised heating system operation leads to improved comfort, fewer breakdowns, longer lifetime, and reduced energy consumption, lower operating costs

Besides these, indirect beneficiaries are:

The tenants and users of the buildings through increased comfort,
The investors of the buildings through more accurate assessment of the lifecycle costs of heating systems and better compliance with emissions regulations,
The employees of the customers through upskilling and more responsibilities.

How does your project idea affect energy savings or CO2 emissions?

The goal of this project is to develop a solution that can be rolled out across the largest number of building types, in particular multi-family homes and smaller office buildings. Based on a portfolio of multi-family homes of our customer, we have seen during the Winter 22/23 that heating systems in multi-family homes can be optimized to achieve a 10-20% lower consumption. According to the SFoE, private households consume 120PJ annually of heating oil and gas for space heating and domestic hot water. This results in CO2 emissions of 8’500kt CO2 annually. Even a small relative improvement will result in a big absolute number.

Additional effects that we have not quantified yet are less gray energy that is required to produce heating systems due to longer equipment lifetime and fewer maintenance truck roles due to fewer equipment breakdowns.

Lastly, we have seen that we were able to reduce the return temperatures for all buildings with district heating. Lowering the return temperatures increases the efficiency and capacity of remote heat distribution networks. This will also result in lower CO2 emissions.

Current status and previous activities

What has been tried before?

There are several current solutions that address either a limited set of customers or limited use cases. Either way, this puts such solutions out of reach for most buildings.

There is the classic energy optimisation (energetische Betriebsoptimierung eBO) service that is offered by engineering companies. An engineer would then use a checklist or manually analyse available data to suggest optimisation procedures.

There are software offerings that target technical monitoring and fault detection and diagnostics (FDD). Such software applies hundreds of rules to individual measurements or system components. The software is not cheap, requires potentially multiple months of set-up, and is meant for users that possess the expertise of system diagnostics.

There are new software offerings that provide Machine Learning- or Artificial Intelligence-based energy optimisation. These typically provide more accurate demand forecasting than the originally installed building control system.

Our own solution, the Plutinsus Visual Analytics solution, is currently used productively by a heating system maintenance provider at over 100 multi-family homes in Switzerland. Plutinsus Visual Analytics is an innovative software for the efficient operational diagnostics of heating systems. It provides currently 60 pre-defined types of analyses and has been designed to work on top of any IoT or monitoring system. These pre-defined analyses do not yet cover all the necessary cases.

Plutinsus has an ongoing collaboration with James Allan to advance the use of diagnostics for the operation of the NEST building at Empa (https://linkedbuildingdata.net/ldac2023/abstracts.html#industry03) based on a digital twin using linked data. This digital twin will contain information about the physical composition of the technical building infrastructure of the NEST building.

What was not successful?

As described above, all these solutions work only in specific cases and are not broadly used due to limited benefits, high costs, or insufficient skills. Most of these solutions involve a single provider and target a single benefit.

Building owners that rent out their space are often not motivated to implement measures that solely address energy efficiency because the energy costs are borne by the tenants.

Current approaches for energy optimisation start with energy consumption measurement using certified equipment. In the case of heat consumption measurements, this requires flow meters that are expensive and costly to be installed into the piping.

Solutions for technical monitoring are sometimes installed during commissioning and testing and then ripped out because they are too costly to operate and do not deliver enough benefits.

In general, current solutions still require too much manual effort and technical skills to be provided at reasonable costs.

Better solutions would require the collaboration of multiple providers. This goes against established business models. And currently everyone is making enough money and is not willing to invest in new solutions.

The Plutinsus solution pre-defines analyses based on component types. This limits the efficiency of analysis for more complex systems without further explicit modelling of sub-systems and systems and of more complicated cause-and-effect relationships. This knowledge has been considered while planning the system but is not available to the diagnostics software.

What have you learned?

It is important that the solution offers sufficient benefits, that the costs are reasonable and that the necessary skills are available.

The solution will have to address multiple benefits at once: assurance of quality of comfort, maximisation of equipment lifetime and predictability of maintenance and replacement costs, minimisation of energy consumption and greenhouse gas emissions, support for planning of retrofits.

We must find synergies to keep the cost down of items that are unique to each site or building and cannot be reducing through scaling:

Hardware to collect measurements should be shared among all interested parties by using open standards.
Monitoring and diagnostic software must become smarter by incorporating knowledge about the functioning and the specific configuration of the technical building infrastructure to completement the skill and expertise of the users and to increase the efficiency and quality of the analysis.
The required information about the specific building set-up that is required to configure such diagnosis system should be created during the planning and installation phases.
The solution should facilitate the collaboration of stakeholders with different skill levels. There will always be issues that will require a highly trained engineer. It is crucial that such skills are available if needed.
The business models of all involved parties should be kept as scalable as possible. It is particularly important to distinguish between product and service business models. This requires a seamless integration of the offerings of multiple solution providers.
An optimised heating system can be achieved through continuous monitoring and commissioning using operational data. This result does not require expensive heat energy measurement. We should focus on results rather than methods.

Resources needed

What are your planned work packages?

The goal of this project is to develop a proof-of-concept that demonstrates how this concept works and how it can be scaled using a representative demonstration site. It should showcase the activities and necessary skills of all involved people, the novel value propositions and business models for the various stakeholders, and the required hardware and software.

Kick off the project.
Define project scope.
Pick a representative demonstration site and set of use cases.
Develop answers to the following questions.
What is the information needed in order to perform the tasks of the use cases?
Who has got the required skills to perform these tasks and how could various people collaborate?
What does the software need to know such that it can calculate the performance metrics, present the information to the user, and guide the analysis?
Who can provide this information?
Apply method to chosen demonstration site.
Conduct and evaluate the test at the demonstration site.
Conclude and disseminate the findings.

How can the Energy Lab help you?

The proposed project requires the collaboration of multiple stakeholders along the value chain. Such coordination is hard to achieve through the initiative of any stakeholder by themselves. We hope that we can find respective partners who plan, install, commission, and operate heating systems, as well as academic partners who can close the gap between what is needed and what is state-of-the-art through the network of the Energy Lab.

Prof. Dr. Olivier Steiger (HSLU Institute for Building Technology and Energy IGE) and Dr. James Allan (Empa Urban Energy Systems Laboratory and NEST building demonstrator) have – tentatively and non-bindingly – offered to be a project partner.

We will use the CHF 20’000 price money to motivate these partners to collaborate with us by covering (some of) their expenses. We foresee that this money is mainly used for the academic partners and for the set-up of the demonstration site.