{"id":14771,"date":"2026-05-22T01:02:04","date_gmt":"2026-05-22T01:02:04","guid":{"rendered":"https:\/\/oxand.com\/en\/blog\/ai-identify-worst-performing-buildings-before-retrofit-programs\/"},"modified":"2026-05-29T02:05:26","modified_gmt":"2026-05-29T02:05:26","slug":"ai-identificeren-slechtst-presterende-gebouwen-voor-retrofitprogrammas","status":"publish","type":"post","link":"https:\/\/oxand.com\/nl\/blog\/ai-identify-worst-performing-buildings-before-retrofit-programs\/","title":{"rendered":"Kan AI helpen bij het identificeren van de slechtst presterende gebouwen v\u00f3\u00f3r grote renovatieprogramma's?"},"content":{"rendered":"\n<p><strong>Yes. AI can help identify the worst-performing buildings in a portfolio before launching retrofit programs.<\/strong> Here&#8217;s how it works:<\/p>\n<ul>\n<li><strong>Data Analysis at Scale:<\/strong> AI reviews energy bills, occupancy patterns, building characteristics, and climate data to find underperforming properties.<\/li>\n<li><strong>Improved Targeting:<\/strong> By pinpointing high-risk buildings, AI helps avoid wasting resources on low-priority assets.<\/li>\n<li><strong>Advanced Models:<\/strong> Techniques like anomaly detection, clustering, and predictive modeling rank buildings by performance and risk.<\/li>\n<li><strong>Data Integration:<\/strong> AI combines diverse data sources &#8211; like utility bills, IoT sensor data, and maintenance logs &#8211; for sharper insights.<\/li>\n<li><strong>Supports Experts:<\/strong> AI narrows focus, ensuring engineers spend time where it&#8217;s most impactful.<\/li>\n<\/ul>\n<p><strong>Key Benefits:<\/strong><\/p>\n<ul>\n<li>Potential energy savings: 10\u201340%.<\/li>\n<li>More accurate predictions: AI models achieve error rates as low as 5%, compared to 18\u201325% for traditional methods.<\/li>\n<li>Long-term financial planning: AI supports multi-year investment strategies, reducing costs and boosting ROI.<\/li>\n<\/ul>\n<p>AI isn\u2019t a replacement for expert audits but acts as a pre-screening tool to guide smarter, data-driven decisions.<\/p>\n<h2 id=\"oaa-webinar-usage-of-ai-and-ir-imaging-in-energy-efficient-retrofits\" tabindex=\"-1\" class=\"sb h2-sbb-cls\">OAA Webinar: Usage of AI and IR Imaging in Energy Efficient Retrofits<\/h2>\n<p> <iframe class=\"sb-iframe\" src=\"https:\/\/www.youtube.com\/embed\/9lzww7LhCHU\" frameborder=\"0\" loading=\"lazy\" allowfullscreen style=\"width: 100%; height: auto; aspect-ratio: 16\/9;\"><\/iframe><\/p>\n<h6 id=\"sbb-itb-5be7949\" class=\"sb-banner\" style=\"display: none;color:transparent;\">sbb-itb-5be7949<\/h6>\n<h2 id=\"why-retrofit-programs-miss-the-worst-performing-buildings\" tabindex=\"-1\" class=\"sb h2-sbb-cls\">Why Retrofit Programs Miss the Worst-Performing Buildings<\/h2>\n<figure>         <img decoding=\"async\" src=\"https:\/\/assets.seobotai.com\/undefined\/6a0f9d59b8967166c8c5eb2f-1779411255577.jpg\" alt=\"AI vs Traditional Methods: Building Retrofit Performance &#038; Accuracy\" style=\"width:100%;\"><figcaption style=\"font-size: 0.85em; text-align: center; margin: 8px; padding: 0;\">\n<p style=\"margin: 0; padding: 4px;\">AI vs Traditional Methods: Building Retrofit Performance &amp; Accuracy<\/p>\n<\/figcaption><\/figure>\n<p>Retrofit programs often fall short because they focus on the wrong targets. This happens primarily due to three main issues: incomplete data, a focus on cost over performance, and the financial impact of poor targeting.<\/p>\n<h3 id=\"gaps-in-data-and-portfolio-visibility\" tabindex=\"-1\">Gaps in Data and Portfolio Visibility<\/h3>\n<p>Data quality across building portfolios is often inconsistent. One property might have detailed energy audits and metering records, while another has little more than its square footage and address on file. This disparity makes it tough to compare buildings fairly.<\/p>\n<blockquote>\n<p>&quot;Retrofit decisions become harder &#8211; not because solutions are unavailable, but because buildings vary in readiness, data quality, and constraints.&quot; &#8211; Schneider Electric <a href=\"https:\/\/blog.se.com\/buildings\/2026\/04\/24\/one-portfolio-many-buildings-a-practical-framework-for-retrofit-decisions-at-scale\" target=\"_blank\" style=\"display: inline;\" rel=\"nofollow noopener noreferrer\"><sup>[3]<\/sup><\/a><\/p>\n<\/blockquote>\n<p>Traditional energy audits can help fill these gaps, but they\u2019re expensive and time-consuming. As a result, decisions often end up being driven by incomplete data, steering efforts away from true performance-based improvements.<\/p>\n<h3 id=\"prioritizing-cost-over-performance\" tabindex=\"-1\">Prioritizing Cost Over Performance<\/h3>\n<p>When budgets are tight, building owners tend to prioritize cheaper retrofits over those that could deliver the best energy savings. This cost-driven approach often leads to unreliable savings predictions. Regression-based planning models, for instance, carry error rates ranging from 18% to 25%, which highlights the financial risks of ignoring performance data <a href=\"https:\/\/preview-www.nature.com\/articles\/s41598-026-36284-w\" target=\"_blank\" style=\"display: inline;\" rel=\"nofollow noopener noreferrer\"><sup>[4]<\/sup><\/a>. In the long run, this mindset can derail efforts to achieve meaningful energy efficiency.<\/p>\n<h3 id=\"the-cost-of-targeting-the-wrong-buildings\" tabindex=\"-1\">The Cost of Targeting the Wrong Buildings<\/h3>\n<p>Focusing on the wrong buildings wastes money and slows progress toward decarbonization goals. Targeting the worst-performing buildings &#8211; the &quot;high savers&quot; &#8211; can reduce total portfolio energy use by 12\u201332% <a href=\"https:\/\/ideas.repec.org\/a\/gam\/jeners\/v14y2021i14p4334-d596837.html\" target=\"_blank\" style=\"display: inline;\" rel=\"nofollow noopener noreferrer\"><sup>[2]<\/sup><\/a>. Missing these opportunities becomes increasingly risky as energy regulations tighten and tenants demand greener spaces. Buildings that fail to meet performance benchmarks may face compliance issues, reduced leasing potential, and declining value.<\/p>\n<p>AI offers a way out of this cycle. By delivering precise, data-driven insights, it can identify the true underperformers within a portfolio, helping owners make smarter decisions.<\/p>\n<blockquote>\n<p>&quot;Tackling energy efficiency is the most tangible path to real estate decarbonization, but many building owners lack a clear roadmap.&quot; &#8211; Ramya Ravichandar, Vice-President of Product Management, Smart Buildings &amp; IOT, JLL <a href=\"https:\/\/www.jll.com\/en-us\/insights\/how-ai-is-boosting-efforts-to-cut-buildings-energy-use\" target=\"_blank\" style=\"display: inline;\" rel=\"nofollow noopener noreferrer\"><sup>[1]<\/sup><\/a><\/p>\n<\/blockquote>\n<h2 id=\"how-ai-pinpoints-high-risk-high-impact-buildings\" tabindex=\"-1\" class=\"sb h2-sbb-cls\">How AI Pinpoints High-Risk, High-Impact Buildings<\/h2>\n<p>For large property portfolios, figuring out where to start can feel like searching for a needle in a haystack. AI steps in by systematically analyzing data to spotlight assets that demand immediate attention.<\/p>\n<h3 id=\"ai-methods-for-screening-large-portfolios\" tabindex=\"-1\">AI Methods for Screening Large Portfolios<\/h3>\n<p>AI uses advanced techniques like anomaly detection to flag unusual energy use, clustering to group similar buildings, and predictive modeling to estimate future energy demand and emissions. These tools help rank assets by risk, making it easier to prioritize action.<\/p>\n<p>One standout approach is risk scoring. This method evaluates buildings based on factors like their exposure to changing regulations and their potential to become stranded assets as energy standards grow stricter. By combining models like <a href=\"https:\/\/en.wikipedia.org\/wiki\/Long_short-term_memory\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" style=\"display: inline;\">LSTM<\/a> (Long Short-Term Memory networks) and <a href=\"https:\/\/en.wikipedia.org\/wiki\/XGBoost\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" style=\"display: inline;\">XGBoost<\/a>, AI captures both time-based patterns and complex interactions among building features. These hybrid models are highly accurate, achieving a root mean square error (RMSE) of under 5%, compared to 18\u201325% for traditional regression models <a href=\"https:\/\/preview-www.nature.com\/articles\/s41598-026-36284-w\" target=\"_blank\" style=\"display: inline;\" rel=\"nofollow noopener noreferrer\"><sup>[4]<\/sup><\/a>.<\/p>\n<p>When multiple data streams are integrated, these risk assessments become even sharper.<\/p>\n<h3 id=\"combining-multiple-data-sources-for-a-full-picture\" tabindex=\"-1\">Combining Multiple Data Sources for a Full Picture<\/h3>\n<p>No single dataset can provide a complete perspective. AI pulls together various inputs, including fundamental building details (like floor area, age, insulation, and HVAC systems), operational data from smart meters and building management systems, maintenance logs from CMMS, and contextual factors like weather patterns and occupancy schedules. Carbon emission records and energy use intensity (EUI) metrics are also layered in to enrich the analysis.<\/p>\n<p>AI excels at handling inconsistent data. For instance, hybrid ensemble models combine high-frequency sensor data from modern buildings with sparser records from older ones <a href=\"https:\/\/link.springer.com\/article\/10.1007\/s41748-026-01113-7\" target=\"_blank\" style=\"display: inline;\" rel=\"nofollow noopener noreferrer\"><sup>[6]<\/sup><\/a>. This is critical because older buildings are about 1.65 times more sensitive to climate-induced energy demand changes compared to newer, nearly zero-energy buildings. By 2050, energy demand is expected to rise by 199.1% in traditional buildings versus 120.7% in more efficient ones <a href=\"https:\/\/link.springer.com\/article\/10.1007\/s41748-026-01113-7\" target=\"_blank\" style=\"display: inline;\" rel=\"nofollow noopener noreferrer\"><sup>[6]<\/sup><\/a>. AI helps quantify this &quot;climate resilience gap&quot;, giving asset managers an early warning about potential financial risks.<\/p>\n<p>This precise screening lays the groundwork for targeted expert evaluations.<\/p>\n<h3 id=\"ai-as-a-screening-tool-not-a-final-decision-maker\" tabindex=\"-1\">AI as a Screening Tool, Not a Final Decision Maker<\/h3>\n<p>AI doesn&#8217;t replace detailed audits &#8211; it narrows the focus to assets that need deeper investigation. This approach keeps costs under control while ensuring engineering expertise is directed where it matters most.<\/p>\n<blockquote>\n<p>&quot;The value of AI lies in its ability to learn the energy demand patterns of building assets and optimize energy distribution.&quot; &#8211; Ramya Ravichandar, Vice-President of Product Management, Smart Buildings &amp; IOT, JLL <a href=\"https:\/\/www.jll.com\/en-us\/insights\/how-ai-is-boosting-efforts-to-cut-buildings-energy-use\" target=\"_blank\" style=\"display: inline;\" rel=\"nofollow noopener noreferrer\"><sup>[1]<\/sup><\/a><\/p>\n<\/blockquote>\n<p>AI also detects subtle issues like &quot;invisible degradation.&quot; For example, it can identify a gradual 2% weekly increase in energy consumption that might go unnoticed during routine inspections <a href=\"https:\/\/oxmaint.com\/industries\/property-management\/ai-asset-health-index-commercial-real-estate\" target=\"_blank\" style=\"display: inline;\" rel=\"nofollow noopener noreferrer\"><sup>[7]<\/sup><\/a>. While AI won&#8217;t replace engineers, it ensures their efforts are focused on areas where retrofits and investments will have the most impact.<\/p>\n<h2 id=\"what-data-ai-models-need-to-work-well\" tabindex=\"-1\" class=\"sb h2-sbb-cls\">What Data AI Models Need to Work Well<\/h2>\n<p>Quality data is the backbone of any AI model aiming to identify high-risk buildings and prioritize retrofit investments. The accuracy and structure of the input data directly determine how reliable the model\u2019s results will be.<\/p>\n<h3 id=\"core-data-inputs-for-ai-analysis\" tabindex=\"-1\">Core Data Inputs for AI Analysis<\/h3>\n<p>AI models rely on foundational data that most building owners already have, such as:<\/p>\n<ul>\n<li><strong>Asset registers<\/strong><\/li>\n<li><strong>Utility bills<\/strong><\/li>\n<li><strong>Architectural drawings<\/strong><\/li>\n<li><strong>Occupancy schedules<\/strong><\/li>\n<li><strong>Maintenance logs<\/strong><\/li>\n<\/ul>\n<p>These data points establish a baseline for understanding a building\u2019s energy usage, operational patterns, and upkeep history.<\/p>\n<p>However, access to detailed records varies. Buildings with limited data can only support basic measures, like general recommendations based on type, size, and location (Tier 3 actions). In contrast, comprehensive data allows for more precise, in-depth analyses, which significantly enhance the accuracy of AI predictions <a href=\"https:\/\/blog.se.com\/buildings\/2026\/04\/24\/one-portfolio-many-buildings-a-practical-framework-for-retrofit-decisions-at-scale\" target=\"_blank\" style=\"display: inline;\" rel=\"nofollow noopener noreferrer\"><sup>[3]<\/sup><\/a>.<\/p>\n<p>Once this foundational data is in place, advanced inputs can take the analysis to the next level.<\/p>\n<h3 id=\"advanced-inputs-that-improve-model-accuracy\" tabindex=\"-1\">Advanced Inputs That Improve Model Accuracy<\/h3>\n<p>Adding more detailed and specific data sharpens AI predictions. For instance:<\/p>\n<ul>\n<li><strong>Weather-normalized energy data<\/strong>: Removes seasonal fluctuations, revealing true efficiency gaps.<\/li>\n<li><strong>Component-level condition records<\/strong>: Includes metrics like thermal transmittance (U-values) for walls and windows, thermographic scans, pipe material and age, and real-time IoT sensor data (e.g., vibration, pressure, and current draw).<\/li>\n<\/ul>\n<p>When AI analyzes multivariate IoT signals &#8211; such as comparing compressor current draw against ambient temperature &#8211; it can detect faults weeks before they cause a breakdown. In fact, some systems can identify issues 3\u20136 weeks in advance <a href=\"https:\/\/www.nature.com\/articles\/s41598-026-41747-1\" target=\"_blank\" style=\"display: inline;\" rel=\"nofollow noopener noreferrer\"><sup>[5]<\/sup><\/a><a href=\"https:\/\/oxmaint.com\/industries\/property-management\/ai-asset-health-index-commercial-real-estate\" target=\"_blank\" style=\"display: inline;\" rel=\"nofollow noopener noreferrer\"><sup>[7]<\/sup><\/a><a href=\"https:\/\/oxmaint.com\/industries\/hvac\/ai-hvac-predictive-maintenance-efficiency\" target=\"_blank\" style=\"display: inline;\" rel=\"nofollow noopener noreferrer\"><sup>[9]<\/sup><\/a>. This kind of proactive maintenance is only possible with continuous, granular, and well-labeled data.<\/p>\n<p>Still, having the right data is only part of the equation. Proper management of that data is key.<\/p>\n<h3 id=\"why-data-governance-matters\" tabindex=\"-1\">Why Data Governance Matters<\/h3>\n<p>Collecting data is one thing, but ensuring it\u2019s usable is another challenge entirely. Issues like inconsistent naming, duplicate entries, and siloed systems across multiple properties can hinder AI\u2019s ability to generate reliable insights. As Schneider Electric Blog puts it:<\/p>\n<blockquote>\n<p>&quot;Consistency matters as much as technical sophistication. Portfolio decision\u2011makers rarely need precise, year\u2011by\u2011year predictions&#8230; What they need is a reliable way to compare options.&quot; <a href=\"https:\/\/blog.se.com\/buildings\/2026\/04\/24\/one-portfolio-many-buildings-a-practical-framework-for-retrofit-decisions-at-scale\" target=\"_blank\" style=\"display: inline;\" rel=\"nofollow noopener noreferrer\"><sup>[3]<\/sup><\/a><\/p>\n<\/blockquote>\n<p>To make AI outputs actionable &#8211; especially when they inform multi-million-dollar retrofit decisions &#8211; data must be centralized, validated, and formatted consistently. Without clear data governance, even the most advanced models can produce results that are hard to trust or implement <a href=\"https:\/\/link.springer.com\/article\/10.1007\/s11831-024-10159-7\" target=\"_blank\" style=\"display: inline;\" rel=\"nofollow noopener noreferrer\"><sup>[8]<\/sup><\/a>.<\/p>\n<p>Establishing portfolio-wide data standards isn\u2019t just a technical formality; it\u2019s essential for unlocking AI\u2019s full potential. Governed data forms the foundation for AI-driven insights that lead to effective and targeted retrofit investments.<\/p>\n<h2 id=\"turning-ai-insights-into-a-retrofit-investment-plan\" tabindex=\"-1\" class=\"sb h2-sbb-cls\">Turning AI Insights into a Retrofit Investment Plan<\/h2>\n<p>Once AI pinpoints high-risk buildings, the next step is transforming those insights into practical investment strategies.<\/p>\n<h3 id=\"from-portfolio-screening-to-scenario-comparison\" tabindex=\"-1\">From Portfolio Screening to Scenario Comparison<\/h3>\n<p>AI\u2019s detailed screening provides a ranked list of buildings based on risk, energy performance, and projected costs. But a ranked list alone doesn\u2019t drive action. To make it actionable, segment the list into tiers: buildings needing immediate attention, those that can wait 2\u20133 years, and properties where minor operational tweaks suffice for now.<\/p>\n<p>From there, use scenario comparisons to weigh different retrofit priorities and timelines. AI tools excel at stress-testing these scenarios against real-world constraints, such as budget limits, carbon reduction goals, and acceptable risk levels. This process helps refine decisions before any actual spending begins, creating a clear path toward a multi-year CAPEX and OPEX model.<\/p>\n<h3 id=\"building-multi-year-capex-and-opex-plans\" tabindex=\"-1\">Building Multi-Year CAPEX and OPEX Plans<\/h3>\n<p>Scenario comparisons lay the groundwork for a detailed investment roadmap. This roadmap evaluates retrofit options across multiple dimensions, including Energy Use Intensity, Return on Investment (ROI), Internal Rate of Return (IRR), Net Present Value (NPV), and projected CO\u2082 savings. These metrics are often modeled over timeframes of 25 years or more <a href=\"https:\/\/www.nature.com\/articles\/s41598-026-41747-1\" target=\"_blank\" style=\"display: inline;\" rel=\"nofollow noopener noreferrer\"><sup>[5]<\/sup><\/a>.<\/p>\n<p>This long-term perspective is crucial. Without lifecycle cost analysis, retrofit decisions might seem cheaper upfront but lead to higher expenses over time. Transitioning from reactive, emergency repairs to a <a href=\"https:\/\/oxand.com\/en\/how-predictive-maintenance-without-iot-and-real-time-brings-value-to-infrastructure-and-building-asset-owners\/\" style=\"display: inline;\">predictive investment model<\/a> not only stabilizes budgets but also minimizes unpleasant financial surprises. Systems built on this approach often deliver ROI within 6\u201312 months <a href=\"https:\/\/oxand.com\/en\/services\/predictive-maintenance-roi\" style=\"display: inline;\"><sup>[10]<\/sup><\/a>.<\/p>\n<h3 id=\"using-asset-management-platforms-like-oxand-simeotm\" tabindex=\"-1\">Using Asset Management Platforms Like <a href=\"https:\/\/oxand.com\/en\/oxand-simeo\/\" style=\"display: inline;\">Oxand Simeo<\/a>\u2122<\/h3>\n<p><img decoding=\"async\" src=\"https:\/\/assets.seobotai.com\/oxand.com\/6a0f9d59b8967166c8c5eb2f\/5cac569925d8fe559a79b42ae0202963.jpg\" alt=\"Oxand Simeo\" style=\"width:100%;\"><\/p>\n<p>Platforms like <a href=\"https:\/\/oxand.com\/\" style=\"display: inline;\">Oxand<\/a> Simeo\u2122 bridge the gap between AI insights and actionable investment plans. With a vast library of <strong>10,000 aging and energy performance models<\/strong> and <strong>30,000 maintenance actions<\/strong>, Oxand Simeo\u2122 simulates asset degradation and intervention costs &#8211; no need for additional hardware or sensors <a href=\"https:\/\/oxand.com\/en\/services\/predictive-maintenance-roi\" style=\"display: inline;\"><sup>[10]<\/sup><\/a>.<\/p>\n<p>Here are two examples of its effectiveness:<\/p>\n<ul>\n<li> <strong><a href=\"https:\/\/www.inli.com\/en\" target=\"_blank\" rel=\"nofollow noopener noreferrer\" style=\"display: inline;\">In&#8217;li<\/a><\/strong>, a French residential property management group, integrated energy performance goals into their investment planning using Oxand Simeo\u2122. Their Head of Budget and Asset Valuation explained:<br \/>\n<blockquote>\n<p>&quot;We turned to Oxand because we needed a tool that would provide us with a predictive &#8211; not just corrective &#8211; view and help us manage our investments more effectively. Oxand stood out for its risk management capabilities.&quot; <a href=\"https:\/\/oxand.com\/en\/services\/predictive-maintenance-roi\" style=\"display: inline;\"><sup>[10]<\/sup><\/a><\/p>\n<\/blockquote>\n<\/li>\n<li> The <strong>Meuse Department in France<\/strong> faced challenges with fragmented data scattered across multiple systems. By consolidating this data through Oxand Simeo\u2122, they created clear, forward-looking investment projections. Their General Director of Services shared:<br \/>\n<blockquote>\n<p>&quot;We needed a tool that would allow us to consolidate the fragmented data we had and project it in a way that could be clearly presented to our elected officials, who are the decision-makers.&quot; <a href=\"https:\/\/oxand.com\/en\/services\/predictive-maintenance-roi\" style=\"display: inline;\"><sup>[10]<\/sup><\/a><\/p>\n<\/blockquote>\n<\/li>\n<\/ul>\n<p>Both examples highlight how data-driven tools can align short-term actions with long-term investment strategies, creating master plans that are both practical and forward-thinking.<\/p>\n<h2 id=\"conclusion-making-ai-a-standard-part-of-retrofit-planning\" tabindex=\"-1\" class=\"sb h2-sbb-cls\">Conclusion: Making AI a Standard Part of Retrofit Planning<\/h2>\n<p>When portfolio screening isn\u2019t reliable, building owners often end up spending money on assets that don\u2019t need immediate attention, while critical, high-risk assets continue to deteriorate. AI flips this script by providing a consistent and scalable way to pinpoint where interventions will make the biggest difference.<\/p>\n<p>The numbers back this up. Research shows that AI-driven decision frameworks can boost performance by up to <strong>53%<\/strong> compared to traditional retrofit planning approaches <a href=\"https:\/\/arxiv.org\/html\/2504.06055v2\" target=\"_blank\" style=\"display: inline;\" rel=\"nofollow noopener noreferrer\"><sup>[12]<\/sup><\/a>. On a larger scale, organizations that move from reactive maintenance to predictive, AI-informed strategies can cut their total cost of ownership by as much as <strong>30%<\/strong> over time <a href=\"https:\/\/oxand.com\/en\/facility-asset-managers\" style=\"display: inline;\"><sup>[11]<\/sup><\/a>.<\/p>\n<p>AI also excels in managing the complexity of real-world scenarios. Whether dealing with assets equipped with detailed metering data or those with only basic information &#8211; like building type, size, or location &#8211; AI delivers accurate results across a wide range of conditions. Its strength lies in consistently handling diverse and complex asset profiles at scale.<\/p>\n<p>Given these insights and the clear advantages of AI, it\u2019s clear that this technology should become a core part of retrofit planning. Rather than being a one-off solution, AI should serve as an ongoing intelligence layer, grounding investment decisions in solid, data-driven insights. Paired with integrated asset management tools, AI empowers building owners to act sooner, allocate resources smarter, and create retrofit programs that remain effective for years to come.<\/p>\n<h2 id=\"faqs\" tabindex=\"-1\" class=\"sb h2-sbb-cls\">FAQs<\/h2>\n<h3 id=\"how-much-building-data-do-i-need-for-ai-screening-to-work\" tabindex=\"-1\" data-faq-q>How much building data do I need for AI screening to work?<\/h3>\n<p>The amount of data needed varies depending on the application, but large datasets are generally the norm. These datasets often include <strong>utility data<\/strong>, <strong>building characteristics<\/strong>, <strong>energy usage<\/strong>, <strong>weather information<\/strong>, and <strong>remote sensing images<\/strong>. For instance, AI tools have been used to analyze facilities covering a massive 40 million square feet. By leveraging metrics like <strong>energy-use intensity<\/strong>, <strong>carbon emissions<\/strong>, and <strong>historical performance<\/strong>, these tools can provide precise and actionable insights.<\/p>\n<h3 id=\"how-do-i-validate-ai-results-before-spending-on-retrofits\" tabindex=\"-1\" data-faq-q>How do I validate AI results before spending on retrofits?<\/h3>\n<p>To make sure AI results are reliable before committing to retrofits, take a few key steps. First, seek <strong>independent validation<\/strong> of the AI&#8217;s capabilities. Check whether it has a track record of success with portfolios similar to yours. Transparency is crucial, so confirm that the measurement and verification (M&amp;V) process is clear and easy to audit.<\/p>\n<p>Ask for references from buildings that are comparable in <strong>size, type, and location<\/strong> to yours. It&#8217;s also important to assess the AI&#8217;s performance across multiple sites, not just a single pilot project. This will give you a better understanding of its consistency and reliability. Most importantly, ensure any savings are backed by a process that can be measured and verified with confidence.<\/p>\n<h3 id=\"how-can-ai-rankings-turn-into-a-3-5-year-retrofit-budget-plan\" tabindex=\"-1\" data-faq-q>How can AI rankings turn into a 3\u20135 year retrofit budget plan?<\/h3>\n<p>AI rankings offer a smart way to shape a 3\u20135 year retrofit budget plan by examining critical factors such as energy efficiency, maintenance requirements, and carbon footprint. This analysis helps pinpoint which buildings need upgrades the most, allowing asset managers to focus their investments where they\u2019ll have the greatest impact. By using a tiered strategy, actions can be organized in a logical sequence, balancing immediate improvements with long-term objectives. This approach ensures resources are used effectively while aligning budgets with sustainability goals.<\/p>\n<h2>Related Blog Posts<\/h2>\n<ul>\n<li><a href=\"\/en\/quick-wins-sustainability-low-capex-actions-portfolio-preparation\/\" style=\"display: inline;\">Quick Wins for Sustainability: Low-Capex Actions That Prepare Your Portfolio for Bigger Moves<\/a><\/li>\n<li><a href=\"\/en\/national-building-renovation-plans-portfolio-investment-strategy\/\" style=\"display: inline;\">National Building Renovation Plans: How to Turn Policy into a Portfolio Investment Strategy<\/a><\/li>\n<li><a href=\"\/en\/worst-performing-buildings-identify-triage-phase-investments-portfolio\/\" style=\"display: inline;\">Worst-Performing Buildings: How to Identify, Triage and Phase Investments Across a Portfolio<\/a><\/li>\n<li><a href=\"\/en\/ai-decarbonisation-investment-planning-building-portfolios\/\" style=\"display: inline;\">How AI Can Support Decarbonisation Investment Planning Across Building Portfolios<\/a><\/li>\n<\/ul>\n<p><script async type=\"text\/javascript\" src=\"https:\/\/app.seobotai.com\/banner\/banner.js?id=6a0f9d59b8967166c8c5eb2f\"><\/script><\/p>\n","protected":false},"excerpt":{"rendered":"<p>AI screent gebouwportefeuilles om slechtst presterende activa te vinden, prioriteiten voor modernisering te rangschikken en meerjarige investeringsplannen op te stellen.<\/p>","protected":false},"author":9,"featured_media":14770,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_seopress_titles_title":"AI Building Screening for Retrofit Priorities","_seopress_titles_desc":"AI screens building portfolios to find worst-performing assets, rank retrofit priorities, and inform multi-year investment plans.","_seopress_robots_index":"","_seopress_robots_follow":"","_seopress_robots_imageindex":"","_seopress_robots_snippet":"","_seopress_robots_primary_cat":"","_seopress_robots_breadcrumbs":"","_seopress_robots_freeze_modified_date":"","_seopress_robots_custom_modified_date":"","_seopress_robots_canonical":"","_seopress_social_fb_title":"","_seopress_social_fb_desc":"","_seopress_social_fb_img":"","_seopress_social_fb_img_attachment_id":0,"_seopress_social_fb_img_width":0,"_seopress_social_fb_img_height":0,"_seopress_social_twitter_title":"","_seopress_social_twitter_desc":"","_seopress_social_twitter_img":"","_seopress_social_twitter_img_attachment_id":0,"_seopress_social_twitter_img_width":0,"_seopress_social_twitter_img_height":0,"_seopress_redirections_value":"","_seopress_redirections_enabled":"","_seopress_redirections_enabled_regex":"","_seopress_redirections_logged_status":"","_seopress_redirections_param":"","_seopress_redirections_type":0,"_seopress_analysis_target_kw":"","footnotes":""},"categories":[1],"tags":[],"customer-name":[],"industry":[],"class_list":["post-14771","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"acf":[],"_links":{"self":[{"href":"https:\/\/oxand.com\/nl\/wp-json\/wp\/v2\/posts\/14771","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oxand.com\/nl\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/oxand.com\/nl\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/oxand.com\/nl\/wp-json\/wp\/v2\/users\/9"}],"replies":[{"embeddable":true,"href":"https:\/\/oxand.com\/nl\/wp-json\/wp\/v2\/comments?post=14771"}],"version-history":[{"count":1,"href":"https:\/\/oxand.com\/nl\/wp-json\/wp\/v2\/posts\/14771\/revisions"}],"predecessor-version":[{"id":14856,"href":"https:\/\/oxand.com\/nl\/wp-json\/wp\/v2\/posts\/14771\/revisions\/14856"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oxand.com\/nl\/wp-json\/wp\/v2\/media\/14770"}],"wp:attachment":[{"href":"https:\/\/oxand.com\/nl\/wp-json\/wp\/v2\/media?parent=14771"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/oxand.com\/nl\/wp-json\/wp\/v2\/categories?post=14771"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/oxand.com\/nl\/wp-json\/wp\/v2\/tags?post=14771"},{"taxonomy":"customer-name","embeddable":true,"href":"https:\/\/oxand.com\/nl\/wp-json\/wp\/v2\/customer-name?post=14771"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/oxand.com\/nl\/wp-json\/wp\/v2\/industry?post=14771"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}