Case Study

Smart energy management for buildings

Identifying product/service growth opportunities in energy management for commercial, office, and residential buildings

CamIn works with early adopters to identify new opportunities enabled by emerging technology.

Revenue:

$5 billion+

Employee headcount:

5,000+

Opportunity:

Energy transition

Service:

Product & service innovation strategy

Sponsored:

Head of Innovation

of CamIn’s project team comprised of leading industry and technology experts

CamIn’s expert team

Our energy utilities client wanted to confirm energy management products & services to decarbonise their customers’ building assets. CamIn went through its proprietary process to confirm 5 quick-win products and services that unlock instant value to new customers within 6 months.

Table of Contents

Our client's problem
CamIn's solution
Example outputs
Key insights

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Industry:

Energy, Power & Utilities

Revenue:

$5 billion+

Employee headcount:

5,000+

Opportunity:

Energy transition

Service:

Product & service innovation strategy

Sponsored by:

Head of Innovation

30,000

For $30,000, we de-risked their $10 million investment into new product opportunities

expert teams

3 external expert teams specialised in energy management and smart buildings

x faster

CamIn completed the work in 6 weeks, 2 times faster than the client’s internal team

Discover more opportunities in

Energy transition

Client's problem

After new regulations were introduced in our client's operating regions in Europe, their customers were looking for solutions to decarbonise their building assets quickly within the next 6 months. Retrofit demand surged 28% especially in the Nordics, highlighting urgent need for effective solutions. The client had developed a concept of product areas, however needed a shortlist of confirmed products and services that were technologically feasible, desired by their customers, and easily implementable. The client lacked the confidence to conduct the necessary analysis within the required breadth, depth, and timeframe. They required CamIn to support them in identifying the correct products for a quick development and launch.

CamIn's solution

Key questions answered

What use cases are emerging globally?
When will this use case be commercially feasible and why? How is value provided to the end customer?
What are the barriers to a successful energy management? What are the main issues with current business models?
What has been done so far to tackle the barriers and what was the outcome?
What vendors are offering solutions?

Our Approach

4	Determined 4 key application areas for commercial, office, and residential building energy management: Monitoring, Demand response, Grid interactivity, Optimisation.
20	Identified and analysed 20 technology use cases in terms of their likelihood of commercialisation, their time to commercialisation, and the scale of their likely impact on the market.
9	Isolated the 9 most promising use cases, principally based on grid interactivity for commercial buildings, based on their high likelihood of commercialisation and their high market impact.
9	Combined the 9 highest-scoring use cases into 5 strategically important product/service areas, in which CamIn will develop business cases for investment.

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Results and Impact

By combining the 9 highest-scoring use cases, CamIn identified 5 strategically important product/service areas for the client.

The client is piloting all 5 products with credible strategic partners and have successfully satisfied current customer demand.

CamIn derisked the client's $10 million investment into now product opportunities, opening new lucrative revenue streams.

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Example Outputs

What are smart energy management solutions for buildings?

Smart energy management solutions enable buildings to monitor, control, and optimise their energy usage in real time. These systems integrate technologies such as sensors, IoT devices, AI-driven analytics, and demand response platforms to improve energy efficiency, reduce carbon emissions, and lower operating costs. In practice, this may include intelligent HVAC control, real-time energy consumption dashboards, predictive maintenance, and grid-interactive capabilities. For utilities, such solutions unlock opportunities to offer value-added services to commercial, office, and residential customers.

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Why is smart energy management important for the sector?

Smart energy management is becoming essential for utilities seeking to remain competitive, compliant, and customer-focused in a rapidly evolving energy landscape.

Enables compliance and accelerates decarbonisation
Drives new revenue and business models
Improves customer engagement and retention
Supports electrification and grid resilience
Delivers fast, measurable impact

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What impact will smart energy management have on the utilities industry?

Over the next decade, smart energy management will fundamentally transform the utilities industry. It will shift the core business model from simply supplying electricity to delivering intelligent, data-driven energy services. This evolution will impact grid operations, customer engagement, regulatory alignment, and long-term competitiveness.

Key impacts over the next 10 years will include:

Decentralised energy orchestration
Real-time grid intelligence
Advanced customer segmentation and personalisation
Participation in dynamic energy markets
Regulatory alignment and ESG compliance
Cost and carbon savings at scale

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Strategic shift for utilities:
Utilities will evolve into hybrid data-energy providers. Those that invest early in smart energy management will gain:

Lower operational costs through automation and optimisation
New customer revenue models via subscription-based energy services
Competitive differentiation in a crowded decarbonisation market
Greater resilience against volatility in supply and demand

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What technologies are emerging for smart energy management?

The landscape of smart energy management is being reshaped by a convergence of digital, connectivity, and control technologies, enabling new levels of visibility, automation, and responsiveness in building energy systems. These emerging technologies support utilities and building operators in achieving ambitious decarbonisation, cost-efficiency, and resiliency goals. The most impactful innovations fall into the following subsegments:

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AI and Advanced Analytics

Artificial Intelligence (AI) and Machine Learning (ML) models are used to predict energy usage patterns, detect inefficiencies, and optimise building systems dynamically.
Natural Language Processing (NLP) enables user-friendly interfaces and energy reporting.
Computer Vision (CV) is applied for occupancy-based control of lighting and HVAC systems.

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IoT and Sensor Networks

Smart sensors collect granular data on temperature, occupancy, air quality, lighting, and energy consumption in real time.
IoT devices enable real-time monitoring, remote control, and diagnostics of distributed energy assets.
Integration with building management systems (BMS) and distributed energy resources (DERs) provides coordinated control.

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Digital Twins and Simulation Technologies

Digital twin platforms create virtual replicas of building systems to simulate energy use, test optimisation strategies, and predict maintenance needs.

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Connectivity and Edge/Cloud Integration

Edge computing allows localised, low-latency decision-making for time-sensitive operations like demand response or fault detection.
Cloud-based energy platforms enable scalable analytics, device orchestration, and integration with enterprise systems (e.g., ERP, CMMS).
5G connectivity enhances the speed and bandwidth for real-time energy telemetry across large campuses or multi-site portfolios.

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Demand Response and Grid-Interactive Technologies

Grid-interactive efficient buildings (GEBs) are equipped with software-defined control systems that respond to signals from the energy grid, adjusting loads in real time.
Automated Demand Response (ADR) capabilities allow buildings to monetise flexibility by reducing or shifting load during peak periods or price spikes.

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Cybersecurity and Resilient Infrastructure

As smart building systems become more connected, cybersecurity architectures including secure boot, network segmentation, and encrypted communications are essential to protect energy infrastructure.
Compliance with frameworks such as ISO 27001, IEC 62443, and NIST Cybersecurity Framework is becoming standard practice.

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Excerpts from the Download

Key drivers of market demand: AI-enabled energy management systems

Smart energy management in buildings is being driven by tightening climate regulations, grid flexibility needs, rising energy costs, digital innovation, and growing ESG, financial, and consumer pressures.

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Building performance and emissions standards: Mandates for near-zero energy buildings and retrofits drive adoption of smart systems. Firms need automation to comply with carbon intensity targets.
Net-zero and decarbonisation commitments: Governments and corporations require verifiable reductions in building emissions. Smart optimisation platforms provide measurable progress toward net-zero, unlocking
Grid flexibility and capacity pressures: Increasing renewable penetration creates grid volatility. Building-level optimisation enables demand response and flexibility services, supporting grid stability and e
District heating and cooling integration: Cities investing in DH/C networks require buildings to return or share excess heat. Automated controls and heat pumps enable efficient integration.
EV adoption and vehicle-to-grid (V2G) growth: EV charging loads challenge building grids but also offer storage capacity. Smart charging and V2G systems monetise flexibility and lower costs.
Energy price volatility and cost control: Spiking energy prices incentivise automated load shifting and on-site optimisation. Building energy management reduces bills by maximising use of local renewables and
Corporate ESG and reporting requirements: Companies must disclose operational emissions and energy efficiency. Verified optimisation data supports sustainability reporting and strengthens green building crede
Tenant and consumer demand for green buildings: Occupants expect lower energy costs, clean energy sourcing, and sustainability credentials. Smart systems enhance building value and market competitiveness.
Digitisation and IoT/AI innovation: Falling costs of IoT sensors, cloud platforms, and AI optimisation make smart energy solutions scalable across building portfolios.
Public incentives and green finance options: Public subsidies and preferential finance require certified energy savings. Verified optimisation systems unlock access to green capital and lower financing costs.

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Example technology use cases within AI-enabled energy management systems

Smart energy management in buildings leverages AI, IoT, digital twins, and advanced controls to optimise energy use, integrate renewables, enable flexibility, and unlock new value streams across grids and districts.

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AI-driven energy optimisation for local renewables and storage: Deploying AI control systems to balance rooftop solar, batteries, and building loads. This approach can maximise self-consumption, reduce grid stress, and cut energy costs.
Smart heat recovery for district heating and integration: Automated building-level heat exchangers and controls return excess or waste heat to district heating networks, improving system efficiency and reducing emissions.
Vehicle-to-building and vehicle-to-grid aggregation: Enabling EV chargers to store and discharge power intelligently. Buildings earn revenue by aggregating EV capacity and providing flexibility services to the grid.
IoT sensors for real-time building energy monitoring: Low-cost sensors monitor HVAC, lighting, and equipment performance. Data feeds predictive algorithms that identify inefficiencies and automate energy savings.
Digital twin of building energy flows: Creating dynamic digital replicas of buildings to simulate energy use and optimise retrofits, demand response, and asset lifecycle management.
Automated demand response participation: Connecting building energy systems to utility demand-response programs. Automated load shifting reduces peak demand charges and earns incentives.
AI-based predictive maintenance of energy assets: Using machine learning to detect anomalies in HVAC, heat pumps, and storage systems, preventing failures and maximising operational efficiency.
Blockchain-enabled energy transactions: Smart contracts record peer-to-peer energy trades between buildings and the grid, creating transparent and auditable local energy markets.
Green building certification and reporting platforms: Energy optimisation systems provide verifiable data for LEED, BREEAM, and ESG reporting, enhancing asset value and compliance with corporate sustainability targets.
Integrated microgrid management platforms : Coordinating solar PV, storage, EVs, and flexible loads at campus or district level. Supports resilience by islanding during grid outages while optimising cost and emissions.

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Example technology use cases within AI-driven energy optimisation for local renewables and storage

Feasibility analysis: highlight estimated commercialisation timeline, development and adoption barriers, and any pilots, acquisitions, and start-ups.
Desirability analysis: quantify where possible the size of the challenge it solves or size of the market it enables. Highlight the emerging opportunities and threats based on market conditions.
Viability analysis: Identify the required capabilities necessary to develop and operate such use case and how good is the capability fit for energy and utilities companies.

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Example feasibility analysis

Time to commercialisation

AI-driven optimisation platforms for distributed energy resources (DERs) are already at high TRL (8-9), with commercially deployed pilots across Europe and North America. Most systems combine IoT sensors, building management platforms, and AI algorithms to balance rooftop solar, storage, and building loads in real time.
Core enabling technologies (IoT sensors, smart inverters, AI optimisation software, cloud/edge analytics) are commercially available. Vendors increasingly are bundling these into Energy Management Systems (EMS) and Virtual Power Plant (VPP) offerings.
Early adopters include university campuses, housing developments, and city districts, where AI-driven optimisation maximises self-consumption of renewables and participates in flexibility markets.
As grid operators increasingly mandate flexibility services and energy costs remain volatile, adoption is expected to accelerate. Commercial maturity is forecast in 1–3 years, with scaling dependent on regulatory frameworks and integration into utility programs.

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Vendors

Stem Inc.: Stem provides AI-driven storage optimisation in commercial and industrial buildings across North America. Their Athena platform uses AI to manage batteries, solar, and building loads, reducing peak demand charges and participating in grid services markets.
SonnenCommunity: Sonnen operates a peer-to-peer energy sharing network in Germany where household batteries and solar are aggregated via AI. The system dynamically optimises self-consumption and trades excess energy, functioning as a distributed VPP.
Brainbox AI: Brainbox AI provides an AI-driven platform that autonomously optimises HVAC energy use in commercial buildings. The platform integrates with legacy HVAC and BMS systems, adapting every 5 minutes using weather forecasts, tariffs, and occupancy data.

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Adoption of AI-driven optimisation for local renewables and storage faces mainly medium-level barriers around cost, integration, regulation, and scalability, giving an overall feasibility score of medium-high.

Barriers to adoption

Difficulty	Barriers to Adoption
High	Installing sensors, controllers, and optimisation software across diverse building portfolios requires significant CapEx, slowing ROI justification.
Medium	Older HVAC, storage, and BMS systems may lack interoperability with AI platforms, requiring costly hardware upgrades or middleware.
Medium	Participation in flexibility and VPP markets depends on evolving regulatory frameworks, limiting near-term revenue certainty for building owners.
Medium	AI optimisation relies on high-quality data from multiple assets (PV, batteries, HVAC, etc.) Lack of standardised data formats complicates integration.
Medium	Connecting building systems to cloud/AI optimisation raises risks of cyberattacks or data breaches, requiring robust protections.
Medium	AI optimisation that shifts loads (e.g., HVAC) must balance energy savings with tenant comfort, otherwise risking user resistance.
Medium	Savings from AI optimisation may depend on volatile energy prices and utility tariffs, limiting confidence in long-term ROI.
Medium	Numerous EMS and VPP providers exist, with different proprietary platforms. Buyers risk lock-in and lack of long-term support.
Medium	To deliver grid services, systems must meet strict communication and control standards (e.g. OpenADR), adding compliance burdens.
Medium	Optimisation strategies may perform well in pilots but face complexity when scaled across hundreds of diverse buildings with varying infrastructure.

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Example desirability analysis

Buildings, campuses, and districts face regulatory, financial, and social pressure to optimise renewable integration and energy efficiency, creating strong demand for AI-enabled optimisation systems.

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Size of opportunity

Globally, the building energy management systems (BEMS) market was valued at $7.1 bn in 2024 and is projected to grow to $20.4 bn by 2033, driven by the need to integrate renewables and storage. The distributed energy resource management systems (DERMS) market, closely aligned with AI-based optimisation, was valued at $0.6 bn in 2024 and is expected to grow to $1.4 bn by 2029.

Europe’s Energy Performance of Buildings Directive (EPBD) revisions mandate smart technologies to reduce energy consumption in all new and renovated buildings by 2030. In the US, the DOE projects a significant growth in commercial buildings adopting grid-interactive efficient technologies by 2030, enabled by AI and automation.

If even 10% of EU’s 220 million buildings adopt AI-driven optimisation at €20-50k per building (one-time system, and ongoing SaaS), the addressable market could reach €40-100 bn, with significant upside in North America and Asia.

In short: AI-driven energy optimisation is technically ready, with rapid scaling likely in 1–3 years as regulation and business models align.

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External threats and opportunities

Factor	Assessment	Overall outlook
Political	EU EPBD, U.S. IRA, and Asian smart building policies drive adoption of AI-enabled energy optimisation.	Positive
Economic	Rising energy prices and volatility incentivise demand-side optimisation; grid services create new revenue streams.	Positive
Social	Tenants and corporates increasingly demand low-energy, green-certified buildings; AI optimisation enhances ESG credentials.	Positive
Technological	IoT, AI, and digital twins are mature and cost-effective; integration with DERMS and VPPs is technically proven.	Positive
Legal	Stricter efficiency codes (EPBD, U.S. state standards) mandate digital optimisation and data reporting for compliance.	Positive
Environmental	Net-zero targets require cuts in emissions; optimisation maximises self-consumption and reduces carbon intensity.	Positive

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Example viability analysis

Utilities are well positioned in terms of regulatory access, customer base, and infrastructure, but must strengthen AI/ML, SaaS scaling, and device-level integration through partnerships or acquisitions.

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Capability fit assessment

Utilities bring strong regulatory, infrastructure, and customer-side expertise, giving them a strong position to adopt AI-enabled optimisation. However, they may lack advanced AI/ML development and SaaS platform capabilities, requiring partnerships with specialised vendors and developers (e.g. Stem, BrainBox AI, AutoGrid) to fill technology and scaling gaps.

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Capability	Rationale	Current fit estimate	Current fit summary
Grid and energy operations knowledge	Deep expertise in managing generation, load balancing, and grid integration.	Strong	Utilities already operate distributed energy and flexibility programs; strong “market access” to customers and regulators.
Regulatory & compliance expertise	Navigating energy market rules, tariffs, demand-response programs.	Strong	Active role in regulatory processes and compliance-heavy markets; able to ensure alignment with grid codes and flexibility services.
Customer & industry partnerships	Partnerships with building owners, municipalities, and corporates.	Strong	Utilities already serve C&I and residential customers; strong channels for adoption of optimisation platforms.
Cybersecurity & data governance	Ensuring resilience, compliance, and customer trust in connected energy systems.	Strong	Utilities already invest heavily in cybersecurity for critical infrastructure; good baseline but must extend to customer systems.
Energy hardware integration (batteries, PV, EV chargers)	Ability to integrate and manage diverse DER assets.	Strong	Existing pilots and partnerships in storage, EV charging, and renewable integration provide a solid foundation.
Data infrastructure & analytics platforms	Handling metering, billing, and energy use datasets.	Moderate	Utilities manage large energy data streams but often lack advanced real-time AI analytics capabilities.
AI/ML optimisation expertise	Development of predictive optimisation algorithms for DERs.	Moderate	Some in-house pilots, but limited compared to specialised AI energy startups (e.g. BrainBox AI, AutoGrid)
Edge computing & IoT integration	Processing and controlling devices (batteries, EVs, HVAC) at scale.	Moderate	Utilities test IoT pilots (smart meters, demand response) but need stronger device-level control capabilities.
Commercial scaling & service models	Offering subscription-based optimisation or SaaS services.	Moderate	Utilities have experience with retail services but limited SaaS platform scaling expertise; may need new business models.
Industrial digitisation experience	Deploying IoT, digital twins, and monitoring in real environments.	Moderate	Strong in metering and asset monitoring; emerging capabilities in digital twin applications for buildings and grids.

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Example Decision Matrix

CamIn has identified and analysed 20 technology use cases in terms of their likelihood of commercialisation, their time to commercialisation, and the scale of their likely impact on the market. Then we have Isolated the 9 most promising use cases, principally based on grid interactivity for commercial buildings, based on their high likelihood of commercialisation and their high market impact. Finally, we have combined the 9 highest-scoring use cases into 5 strategically important product/service areas, in which CamIn will develop business cases for investment.

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