Companies are increasingly adopting AI tools to identify areas of inefficiency and optimize their operations, as they aim to reduce energy consumption throughout their real estate assets.
A significant majority of buildings in developed cities – over 90% – were constructed more than ten years ago, resulting in a substantial gap between their energy performance and current best practices. To achieve a meaningful reduction in carbon emissions over the next decade, it is crucial to prioritize the improvement of building energy efficiency.
The use of sensors and smart technologies in buildings has led to a wealth of real-time data regarding systems and equipment. This data can be leveraged by AI to enhance and optimize energy efficiency.
Energy efficiency is widely regarded as the most direct path to real estate decarbonization, but many building owners are without a clear plan. According to Ramya Ravichandar, Vice-President of Product Management, Smart Buildings & IoT, the value of AI lies in its ability to analyze energy demand patterns and optimize energy distribution across building assets.
Revolutionizing Energy Efficiency for the Future
In the fields of energy audits and modeling, AI is already playing a major role.
Advanced products available today enhance energy audits by uncovering energy-saving and cost-reducing opportunities, while also modeling how demand might change in response to weather disruptions.
AI-driven solutions are transforming energy audits by analyzing diverse data sources to develop predictive maintenance algorithms and optimize HVAC systems. This enables facilities managers to create energy efficiency parameters that prioritize both energy savings and tenant comfort, according to Vidhya Balakrishnan, Vice President of Software Engineering at JLL.
Through the analysis of occupancy and external factors, JLL’s Hank platform optimizes HVAC performance, reducing energy consumption by 20%. It also intelligently reduces energy use during peak demand times, contributing to cost reductions while keeping tenants comfortable.
Benchmark energy models created by AI tools help owners utilize existing building data in their energy strategies. These models also identify opportunities for energy savings across multiple assets, avoiding the need for individual audits that can be time-consuming and expensive.
AI can combine factors like location, climate, energy sources, and external data to model energy consumption for comparable assets or newer technologies that may not have detailed data available. This provides building operators with the ability to leverage advanced energy controls prior to completing a full audit, as explained by Yuehan Wang, Global Research Associate at JLL.
When it comes to energy planning, AI tools also play a key role in developing strategies that blend renewable energy with conventional sources and battery storage, thereby improving resilience in times of price increases and power disruptions.
Sustainable Building Upgrades for a Carbon-Free Future
As buildings face rising tenant demand for sustainable environments and stricter regulatory frameworks, energy retrofits are becoming indispensable for maintaining competitiveness.
JLL’s research shows that increased energy efficiency attracts a “green premium” from tenants who prioritize sustainability, providing long-term value for real estate portfolios.
According to JLL, light to medium retrofits, encompassing upgrades to lighting and mechanical systems, have the potential to decrease energy consumption by as much as 40%.
As AI continues to evolve, it is providing owners and investors with the tools to make more informed decisions about energy retrofits, addressing uncertainties around cost-effectiveness and payback periods with data-driven precision.
To achieve net-zero goals, building retrofits require precise guidance. AI-driven energy models provide this by simulating building energy demand across different retrofit options. These models are crucial for creating detailed digital twins that inform and accelerate the retrofit process.
As building owners strive to achieve decarbonization targets within the next five years, mitigating devaluation and stranding risks becomes crucial. Balakrishnan emphasizes that AI-powered energy modeling can facilitate a data-driven investment strategy, supporting the development of effective retrofit plans for both individual buildings and entire portfolios.
Addressing the Challenges of AI Implementation
Despite recognizing AI’s potential for enhancing energy efficiency, many real estate owners face significant challenges in implementing AI solutions.
Successful AI implementation, according to Wang, requires a significant shift in organizational culture and operations. This includes reorganizing building workflows to effectively utilize AI and fostering active engagement from all employees within the organization.
The adoption of AI for energy efficiency is being driven by several factors. Green leases, which align tenant and owner interests on sustainability goals, play a crucial role. Additionally, government subsidies, including the U.S. Inflation Reduction Act and the EU Green Deal, are making AI implementation more financially viable.
However, wider-reaching incentives are crucial to encourage all building stakeholders to embrace AI adoption.
Ravichandar asserts that mounting regulatory pressure is fueling investor interest in AI-powered solutions for energy efficiency. He advocates for stronger incentives to facilitate broader AI implementation throughout the entire organizational structure.
Although the current level of AI adoption for energy management differs significantly across regions and local governments, the expanding availability of AI products and the declining costs associated with their implementation are expected to foster broader adoption and encourage a shift towards AI-driven energy management solutions.
As the energy sector undergoes a dramatic transformation with the rise of new sources and AI-powered innovations, the strategic value of AI in energy planning is undeniable. This is especially critical for real estate owners and investors who must adapt to ensure their properties remain viable and valuable in the future, as highlighted by Balakrishnan.
This has the potential to convince more companies that AI is the key to realizing their decarbonization goals.
Ravichandar predicts that AI will revolutionize building operations. He emphasizes that the technology is readily available and that the focus now shifts towards integrating AI seamlessly into existing processes and empowering individuals to fully leverage its capabilities.
