Generating Building-Level Heat Demand Time Series by Combining Occupancy Simulations and Thermal Modeling

TL;DR

This work tackles the lack of high-resolution, building-level hourly heating-demand data by introducing a public-data–driven framework that blends a simplified physics-based thermal representation with stochastic occupancy generated via MCMC. The approach yields hourly heating profiles for every building and demonstrates its utility through a Puertollano case study, enabling retrofit impact analysis and district heating planning while preserving data accessibility. Validation shows the method produces plausible daily and hourly patterns that align with temperature correlations and literature benchmarks, and it can capture the diversity of building stock when aggregating profiles. The framework offers a scalable, open-data pathway for data-driven energy-system optimization and cross-sector planning across Europe, with potential enhancements from richer thermal-property data and microclimate considerations.

Abstract

Despite various efforts, decarbonizing the heating sector remains a significant challenge. To tackle it by smart planning, the availability of highly resolved heating demand data is key. Several existing models provide heating demand only for specific applications. Typically, they either offer time series for a larger area or annual demand data on a building level, but not both simultaneously. Additionally, the diversity in heating demand across different buildings is often not considered. To address these limitations, this paper presents a novel method for generating temporally resolved heat demand time series at the building level using publicly available data. The approach integrates a thermal building model with stochastic occupancy simulations that account for variability in user behavior. As a result, the tool serves as a cost-effective resource for cross-sectoral energy system planning and policy development, particularly with a focus on the heating sector. The obtained data can be used to assess the impact of renovation and retrofitting strategies, or to analyze district heating expansion. To illustrate the potential applications of this approach, we conducted a case study in Puertollano (Spain), where we prepared a dataset of heating demand with hourly resolution for each of 9,298 residential buildings. This data was then used to compare two different pathways for the thermal renovation of these buildings. By relying on publicly available data, this method can be adapted and applied to various European regions, offering broad usability in energy system optimization and analysis of decarbonization strategies.

Figures (13)

• Figure 1: Schematic illustration of required data sources and the data processing workflow. The enumerated steps correspond to the steps in Section \ref{['sec:step1']}.
• Figure 2: Thermal model for generating heating demand load profiles. $T_\mathrm{in}(t)$ = hourly actual indoor temperature in , $T_\mathrm{out}(t)$ = hourly outdoor temperature in , $T_\mathrm{set}(t)$ = hourly set temperature based on occupancy and daytime in , $k$ = thermal storage capacity in k W h K, $G$ = thermal conductance in k W K, $\dot{Q}_\mathrm{heating,max}$ = maximum heating power in k W, $\dot{Q}_\mathrm{losses}(t)$ = heat losses in k W, $\dot{Q}_\mathrm{gain}(t)$ = heat gain by internal sources in k W, $\dot{Q}_\mathrm{solargain}(t)$ = heat gain by solar irradiation in k W, $\dot{Q}_\mathrm{heating}(t)$ = actual heating power in k W.
• Figure 3: Illustration of the convergence of single discrete active occupancy profiles (a), towards an average active occupancy profile (b).
• Figure 4: Relation between heating demand and the outside temperature and active occupancy. Peaks in the heating demand correlate with low outside temperature and high active occupancy, leading to the characteristic morning and evening peaks.
• Figure 5: Yearly heating demand on building level in k W h . For better comparison, the color scale was adapted to Ref. MartinConsuegra2018.