Using Models for Energy Performance Analysis

42.1% error reduction with 85% less data. At Ento, we use a lot of traditional black-box Machine Learning to model building energy consumption, and they're great for many use cases. But they have their limits. When we're dealing with: - Plenty of indoor sensor data - Limited historical data - The need to actively control a building’s HVAC system ... plain black-box approaches often fall short. That’s why I’ve been following key trends around blending data-driven methods with physical modeling: 🔹 Transfer Learning: Use data from similar buildings to improve models. 🔹 Digital Twins: Blend data-driven methods and physical simulations. 🔹 Physics-Informed AI: Embed physical laws into the learning process to improve results. Just last month, three papers in these fields came out from leading researchers: - GenTL: A universal model, pretrained on 450 building archetypes, achieved a 42.1% average error reduction when fine-tuned with 85% less data. From Fabian Raisch et al. - An Open Digital Twin Platform: Han Li and Tianzhen Hong from LBNL built a modular platform that fuses live sensor data, weather feeds, and physics-based EnergyPlus models. - Physics-informed modeling: A new study proved that Kolmogorov–Arnold Networks (KANs) can rediscover fundamental heat transfer equations. From Xia Chen et al. Which of these 3 trends do you see having the biggest real-world impact in the next 2-3 years?

I have spent nearly twenty years building energy system models. Continental-scale at granular spatial scales. Hourly (or finer) temporal resolution. Co-optimising generation, storage, transmission, distributed energy resources (DERs), and demand simultaneously. Thousands of scenarios. I have published in Nature Climate Change, Science and PNAS. My work has over 4,300 academic citations. Here is what I have learned: the tools most organisations still use to plan energy systems are not fit for the decisions ahead. Most capacity expansion models optimise generation only. They bolt on storage as an afterthought. They treat the transmission network as a copper plate or a simplified transport model. They run on annual energy balances, missing the hourly dynamics that determine whether the system actually works. They assume stable, predictable fuel prices. The last four weeks have demonstrated why every one of those assumptions is dangerous. When gas was £30/MWh, a model that ignored fuel price volatility produced a plausible answer. At £67/MWh and rising, with Ras Laffan physically destroyed, with the BoE pricing rate hikes instead of cuts, with the Ofgem cap headed for £2,000+, the same model produces an answer that could lead to billions in misallocated capital. What we actually need: models that co-optimise across the whole system (generation, storage, transmission, DERs, demand) at nodal or zonal resolution with sub-hourly dispatch, weather-synchronised across wind, solar, and demand, with stochastic fuel prices that reflect the world we actually live in. Where you build matters as much as what you build. A wind farm in northern Scotland connected to a constrained transmission corridor produces curtailed energy and consumer costs. The same wind farm sited where the grid has capacity produces revenue and system value. The UK is making decisions right now about grid investment, generation siting, storage deployment, and demand connections that will lock in infrastructure for decades. The grid queue reform, the Clean Power 2030 target, the SSEP, the data centre surge, the Hormuz shock. These are not separate problems. They are one system. The planning tools need to catch up with the reality. #EnergyModelling #EnergyTransition #UKEnergy #PowerSystems #CleanEnergy #RenewableEnergy #GridReform #EnergyPolicy #NetZero #EnergyStorage #CapacityExpansion #SystemPlanning

Building Simulation cover article Informing electrification strategies of residential neighborhoods with urban building energy modeling Electrifying end uses is a key strategy to reducing GHG emissions in buildings. However, it may increase peak electricity demand that triggers the need to upgrade the existing power distribution system, leading to delays in electrification and needs of significant investment. There is also concern that building electrification may cause an increase of energy costs, leading to further energy burden for low-income communities. This study uses the urban scale building modeling tool CityBES to assess the electrification impacts of more than 43,000 residential buildings in a neighborhood of Portland, Oregon, USA. Energy efficiency upgrades were investigated on their potential to mitigate the increase of peak electricity demand and energy burden. Simulation results from the calibrated EnergyPlus models show that electrification with heat pumps for space heating and cooling as well as for domestic water heating can reduce CO2e emissions by 38%, but increase peak electricity demand by about 9% from the baseline building stock. Combining electrification measures and energy efficiency upgrades can reduce CO2e emissions by 48% while reducing peak electricity demand by 6% and saving the median household energy costs by 28%. City and utility decision makers should consider integrating energy efficiency upgrades with electrification measures as an effective residential building electrification strategy, which significantly reduces carbon emissions, caps or even decreases peak demand while reducing energy burden of residents. Details of the research can be found at https://lnkd.in/gSCi-W3k The article is co-authored by Tianzhen Hong, Sang Hoon Lee, Wanni Zhang, Han Li, Kaiyu Sun & Joshua Kace             #BuildingSimulation #CityBES #decarbonization #electrification #cover

Calibrating urban building energy models (UBEM) with limited and diverse fidelity of measured energy data has been a challenge. In our study just published at Applied Energy journal, we developed a new framework for calibrating UBEM using smart meter data, targeting the accurate prediction of summer peak electricity loads to support robust grid planning. The framework first integrates various data sources to enhance baseline input assumptions for building models, and then calibrates the baseline models through a pattern-matching approach. A case study using CityBES and two years of AMI data from over 9000 residential customers in Portland, Oregon, demonstrated the workflow and its effectiveness. The calibrated models achieved a daily peak load mean absolute percentage error of 2.6% during the heatwave in the calibration year, and 2.0 % in the validation year using another year of AMI data. Read details at the open access article https://lnkd.in/gCAZ5_jH Authors: Wanni Zhang, Kaiyu Sun, Han Li, Luis Rodríguez-García, Miguel Heleno, Tianzhen Hong This work is funded by the Office of Electricity, U.S. Department of Energy. We appreciate the collaboration and support from Portland General Electric.