# prescriptivedata.io > AI-optimized mirror of prescriptivedata.io containing 49 pages totalling 14,929 words of clean markdown content, structured data, and semantic HTML. Original source: https://prescriptivedata.io/. Last updated: 2026-06-15T01:27:12.157Z. Each page is available as HTML (with JSON-LD structured data) and Markdown (text-only, ideal for LLMs and RAG). ## Homepage - [Make Real Estate Data Actionable](/content/site-root.html) (632 words) ## Articles & Blog Posts - [In The News](/content/in-the-news/category/facilities-dive/index.html) (25 words) - [results/index.html](/content/results/index.html) (1 words) - [legal/index.html](/content/legal/index.html) (1 words) - [research/academic/index.html](/content/research/academic/index.html) (1 words) - [videos/index.html](/content/videos/index.html) (1 words) - [awards/index.html](/content/awards/index.html) (1 words) - [case-studies/index.html](/content/case-studies/index.html) (1 words) - [Make Real Estate Data Actionable](/content/try/index.html) (295 words) - [In The News](/content/in-the-news/category-hacksummit/index.html) (254 words) - [Advisory Team](/content/about/advisory-team/index.html) (738 words) - [U.S. Government Conducts Real Estate Artificial Intelligence Testing For Carbon Emission Reduction - Concludes $28.7M In Possible Annual Energy Savings](/content/press-releases/us-government-conducts-real-estate-artificial-intelligence-testing-for-carbon-emission-reduction-concludes-287m-in-possible-annual-energy-savings.html): NEW YORK – October 6, 2024 – The U.S. General Services Administration, otherwise known as the U.S. government’s national real estate landlord, in collaboration with the U.S. Department of Energy conducted a multi-year study analyzing Prescriptive Data’s Nantum OS as their Energy Management Informat, (1,344 words) - [Prescriptive Data And Jamestown Partner To Provide Carbon Cutting Smart Building Solutions At Waterfront Plaza](/content/press-releases/prescriptive-data-and-jamestown-partner-to-provide-carbon-cutting-smart-building-solutions-at-waterfront-plaza.html): The San Francisco Office Campus Will Use Artificial Intelligence to Reduce Its Carbon Footprint, (943 words) - [NANTUM AI](/content/software/index.html) (67 words) - [Academic Research](/content/research/academic/category-aceee/index.html) (879 words) - [In The News](/content/in-the-news/category-s-p-global-market-intelligence/index.html) (233 words) - [Smart Buildings 2025: Market Trends, Vertical Strategies and Vendor Positioning](/content/in-the-news/451-group-smart-buildings-2025/index.html): The smart building market is undergoing rapid transformation driven by evolving vertical dynamics, accelerating technology innovation and shifting vendor strategies. Vendors are responding with holistic, software-centric platforms, strategic partnerships and an increased focus on interoperability an, (248 words) - [Academic Research](/content/research/academic/category-cell-reports-sustainability/index.html) (534 words) - [Academic Research](/content/research/academic/category-building-and-environment/index.html) (679 words) - [Human Building Interaction Through Large Language Model](/content/research/academic/human-building-interaction-through-large-language-model.html): Large Language Model (LLM) offers opportunities to enhance Human-Building Interaction (HBI) by enabling more direct interactions through intuitive interfaces to complex smart building systems of systems. These systems can be characterized by the vast amounts of data across multiple formats, the lack, Large Language Model (LLM) offers opportunities to enhance Human-Building Interaction (HBI) by enabling more direct interactions through intuitive interfaces to complex smart building systems of systems. These systems can be characterized by the vast amounts of data across multiple formats, the lack of nonconfidential and generalizable information, and the requirement of domain expertise for interpretation. Applying LLMs to domain-specific tasks like HBI also presents additional challenges. Limited training data makes traditional fine-tuning approaches less practical. Meanwhile, the opacity of LLM training data requires careful integration of domain knowledge to ensure reliable responses. Additionally, different LLMs exhibit varying alignment characteristics, suggesting that achieving both natural interaction and technical accuracy requires a multi-agent approach. These challenges highlight the need for innovative approaches to adapt LLMs for specialized domains while maintaining both accuracy and user engagement. In this paper, we develop a zero-shot LLM-based multi-agent system framework for HBI that addresses these challenges, enabling scalable implementation in smart buildings through integration with real-time databases, code repositories, and technical documents. The developed framework has been successfully trained, tested, and validated using a data set from more than 200 commercial buildings. Results tested on the HBI domain demonstrate the effectiveness in providing accurate and contextual responses for diverse users including stakeholders, from tenants to building managers, across various building system applications. (251 words) - [Prescriptive Data and JPMorgan Chase Optimize the Firm’s Energy Use and Sustainable Operations with Nantum OS Technology](/content/press-releases/prescriptive-data-and-jpmorgan-chase-optimize-the-firms-energy-use-and-sustainable-operations-with-nantum-os-technology.html): Nantum OS will bring real-time artificial intelligence and machine learning to help JPMorgan Chase operate its real estate more efficiently NEW YORK – April 14, 2022 – Today, Prescriptive Data announced that JPMorgan Chase has started to use its Nantum OS software to help the global financial ser, (765 words) - [Academic Research](/content/research/academic/category-coming-soon/index.html) (508 words) - [Your Future Begins @ Nantum AI](/content/careers/index.html) (242 words) - [In The News](/content/in-the-news/category-citybiz/index.html) (248 words) - [Cybersecurity](/content/cybersecurity/index.html) (633 words) - [Academic Research](/content/research/academic/category-asme/index.html) (512 words) - [Academic Research](/content/research/academic/category-energy-and-ai/index.html) (508 words) - [In The News](/content/in-the-news/category-thesis-driven/index.html) (158 words) - [NANTUM AI](/content/about/index.html) (67 words) - [Prescriptive Data Selected By KPMG For Climate Accounting & Carbon Trading Program](/content/press-releases/prescriptive-data-selected-by-kpmg-for-climate-accounting-amp-carbon-trading-program.html): Nantum OS, Prescriptive Data’s flagship software, will be deployed as part of KPMG’s Climate Accounting Infrastructure capability, (622 words) - [Real-time Greenhouse Gas Emission Intensity Informed Demand-side Load Regulation For Power Grid Decarbonization](/content/research/academic/real-time-greenhouse-gas-emission-intensity-informed-demand-side-load-regulation-for-power-grid-decarbonization.html): The US targets net-zero-carbon electricity by 2035, emphasizing the need for both supply- and demand-side strategies in the power sector. Our study focuses on demand-side carbon reduction via load regulation, using real-time greenhouse gas emission data to optimize electricity use without reducing o, The US targets net-zero-carbon electricity by 2035, emphasizing the need for both supply- and demand-side strategies in the power sector. Our study focuses on demand-side carbon reduction via load regulation, using real-time greenhouse gas emission data to optimize electricity use without reducing overall consumption. Our multi-scenario analysis indicates significant potential for carbon footprint reduction by adapting strategies to specific grid characteristics. For example, California could have 32.58% more emission reduction with annual instead of quarterly optimization. In grids with diverse generation resources, expanding adjustment ranges from ±5% to ±8% can boost carbon reductions from 1.19% to 1.64%. Our results also reveal low greenhouse gas intensity fluctuation areas as more cost sensitive in electricity consumption than high greenhouse gas intensity fluctuation areas, providing essential insights for policymakers. (382 words) - [Demand-Side Management Under Real-Time Greenhouse Gas Emission Factor for Electricity](/content/research/academic/demand-side-management-under-real-time-greenhouse-gas-emission-factor-for-electricity.html): Electric power generation contributes to the second largest share of greenhouse gas (GHG) emissions in the US. The direct and indirect carbon emissions created from generating electricity vary from resources of generation, and power plant efficiency. Approximately 60% of the electricity comes from b, Electric power generation contributes to the second largest share of greenhouse gas (GHG) emissions in the US. The direct and indirect carbon emissions created from generating electricity vary from resources of generation, and power plant efficiency. Approximately 60% of the electricity comes from burning fossil fuels, mostly coal and natural gas, emitting more than 1500 million metric Tons of 𝐶𝑂! per year. Depending on regions and time of day, the cleanness of electricity significantly varies as more and more intermittent renewable energy resources, such as solar and wind, being added into the grid. With the growing awareness and regulations on GHG emissions, the need for accurate carbon measurement and technologies that reduces GHG for both the supply and demand side is ever-increasing. To optimally control demand-side users such as buildings, balance supply and demand, and incorporate energy storage technologies to reduce overall GHG emissions, the real-time emission factor and its predictions play critical roles. Here, we use the open-source real-time electricity GHG Emission Factor that covers all states in the US and four sample buildings’ demand data from Nantum OS across different regions to propose an optimization framework for potential emission reduction through load shifting. This study highlights the importance to raise awareness, monitor, and account for realtime GHG emissions. Furthermore, it proves the viability to control buildings with electric energy storage system to reduce carbon emissions for demand-side users. (307 words) - [Actionable Energy Insights](/content/software/insights/index.html) (123 words) - [Using PeopleHour For Occupant-Centric Office Building Performance Assessment](/content/research/academic/using-peoplehour-for-occupant-centric-office-building-performance-assessment.html): Measuring and benchmarking office building performance is crucial for enhancing energy efficiency, reducing environmental impact, and improving occupant productivity. Traditional Energy Use Intensity (EUI) metrics and benchmarking methods developed based on them have limitations in accounting for fa, Measuring and benchmarking office building performance is crucial for enhancing energy efficiency, reducing environmental impact, and improving occupant productivity. Traditional Energy Use Intensity (EUI) metrics and benchmarking methods developed based on them have limitations in accounting for factors like occupancy, can hardly be explainable, and lack evolution with the advent of more real-time data. This paper introduces a set of metrics for building performance based on PeopleHour, which incorporates both the number and duration of occupancy to provide a more occupant-centric perspective on office building performance. By adjusting EUI and other related metrics to reflect building performance normalized by occupancy, we offer a more accurate measure of office building efficiency. Using sample office building data from Nantum OS, we demonstrate how PeopleHour-adjusted metrics reveal insights that traditional methods may overlook, particularly during significant occupancy changes before and after the COVID-19 pandemic. This approach emphasizes the importance of occupancy-driven operations especially as the shift of work mode and office building uses after the pandemic. It suggests that PeopleHour can enhance energy benchmarking practices, leading to more informed decisions for improving building performance across various sectors. (286 words) - [Design With AI](/content/actionable-insights/index.html) (124 words) - [Building Automation](/content/software/automation/index.html) (148 words) - [Co-Optimize Condenser Water Temperature and Cooling Tower Fan Using High-Fidelity Synthetic Data](/content/research/academic/co-optimize-condenser-water-temperature-and-cooling-tower-fan-using-high-fidelity-synthetic-data.html): This paper introduces a novel method for optimizing HVAC systems in buildings by integrating a high-fidelity physics-based simulation model with machine learning and measured data. The method enables a real-time building advisory system that provides optimized settings for condenser water loop opera, This paper introduces a novel method for optimizing HVAC systems in buildings by integrating a high-fidelity physics-based simulation model with machine learning and measured data. The method enables a real-time building advisory system that provides optimized settings for condenser water loop operation, assisting building operators in decision-making. The building and its HVAC system are first modeled using eQuest. Synthetic data is then generated by running the simulation multiple times. The data are then processed, cleaned, and used to train the machine learning model. Machine learning model enables real-time optimization of the condenser water loop using particle swarm optimization. The results deliver both a real-time online optimizer and an offline operation look-up table, providing optimized condenser water temperature settings and the optimal number of cooling tower fans at a given cooling load. Potential savings are calculated by comparing measured data from two summer months with the energy costs the building would have experienced under optimized settings. Adaptive model refinement is applied to further improve accuracy and effectiveness by utilizing available measured data. The method bridge between simulation and real-time control. It has the potential to be applied to other building systems, including the chilled water loop, heating systems, ventilation systems, and other related processes. Combining physics models, data models, and measured data also enables performance analysis, tracking, and retrofit recommendations. (267 words) - [In The News](/content/in-the-news/category-unite-ai/index.html) (27 words) - [In The News](/content/in-the-news/category-syracuse-university/index.html) (21 words) - [In The News](/content/in-the-news/category-the-university-of-texas-dallas/index.html) (33 words) - [Make the world a better place,](/content/about/us/index.html) (266 words) - [In The News](/content/in-the-news/category-facilities-dive/index.html) (25 words) - [AI Recommendations](/content/software/engineering/index.html) (109 words) - [ESG IoT Operations](/content/software/intelligence/index.html) (122 words) - [Headquarters](/content/contact-us/index.html) (41 words) - [Shopping Cart](/content/cart/index.html) (10 words) - [press-releases/index.html](/content/press-releases/index.html) (1 words) - [Johnson Controls acquires Nantum AI to accelerate AI-driven energy optimization and control capabilities within OpenBlue](/content/press-releases/johnson-controls-acquires-nantum-ai-to-accelerate-ai-driven-energy-optimization-and-control-capabilities-within-openblue.html):   Acquisition expands Johnson Controls’ AI-powered offerings to help customers further reduce energy use, emissions and operating costs Nantum AI’s algorithms add a new layer of advanced control capabilities to OpenBlue’s existing energy optimization performance MILWAUKEE, Apr, (1,013 words) - [Smart Buildings 2025: Market Trends, Vertical Strategies and Vendor Positioning](/content/in-the-news/category-451-research-group/index.html) (233 words) ## Resources - [Full Page Index](/index.html): Browse all cached pages with rich metadata - [About This Cache](/content/about.html): Methodology, technical details, and usage guidelines - [XML Sitemap](/sitemap.xml): Machine-readable sitemap for crawler discovery - [Robots.txt](/robots.txt): Crawler directives