⚡ SPACER-HB Framework

Spatial Pre-feasibility Assessment for Community Energy, Renovation, and Household Benefits

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What is SPACER-HB?

SPACER-HB is an ad-hoc, spatial, and public-data-based methodological framework for early-stage local energy community (EC) planning. It integrates solar rooftop and buildings' facade renovation potential inventory with household-level energy vulnerability mapping to support informed pre-feasibility decisions. It enables exploration of energy community scenarios and identification of priority areas, without requiring detailed local data or complex models.

Main purpose of the framework:

1. To provide an adaptable set of custom tools that help in spatial assessment of the potential for local energy communities. It utilizes publicly available data and open-source tools to estimate building energy demand, the impacts of renovations, and solar rooftop potential.
2. The framework combines statistical census data (LAU2, LAU1) with building-level information in a unified data workflow to estimate community solar self-consumption, average household savings, and the effects of various pricing rules—using only publicly available data and no private metering.

🧭 Framework Overview

• Python libraries and Anaconda environment:

  Python ver used: 3.9.16

  Anaconda environment snapshot with pip installed packages: spacer-hb_environment.yml

• QGIS and Whitebox plugin:

  QGIS version used: 3.32.3

  Whitebox tools: Whitebox Workflows for Python

  More about Whitebox products open-source/commercial licence: here

• LoadProfileGenerator Tool: Version:10.10

  Database: Adjusted example used for case study

!! IMPORTANT: It is important to ensure the same coordinate system from the beginning !!

Working directory set-up by default built in a simple way, i.e it is assumed to be a single project (i.e: single Case Study per root directory tree).

The framework was developed as part of a PhD dissertation titled "Exploring and Supporting Energy Communities through Spatially Integrated Methods in Local Energy Planning."

It integrates various aspects, such as building stock geometric inventory, energy flexibility, and renewable energy deployment estimation. As a methodological proposal and toolkit for ECs planning, it expands upon the published study:

O. Husiev, O. U. Arrien, and M. Enciso-Santocildes, “Spatial analysis of energy communities and energy vulnerabilities in Spain,” Utilities Policy, vol. 95, p. 101954, Aug. 2025, doi: 10.1016/j.jup.2025.101954

The framework links census-level statistical boundaries with building-specific data, combining geospatial vector/raster layers and tabular inputs. Python-based automation scripts streamline several core processes. While this project does not include full front-end/backend integration or database infrastructure, it builds on open-source tools otherwise available typically as commercial software or requiring large, detailed datasets.

The central case study used to build and partially verify the framework’s outputs is the Otxarkoaga neighborhood in Bilbao, Northern Spain. This case has also been featured in the following scientific publications:

• doi: 10.1016/j.enbuild.2014.10.018
• doi: 10.1016/j.enconman.2016.08.042
• doi: 10.1016/j.enbuild.2023.113288
• doi: 10.1016/j.scs.2021.102787

Methodology & Structure

This framework is implemented through a customized set of Python tools and methods, integrated within a Jupyter Notebook: 📓 main_jp_notebook.ipynb

📁 Project Directory Principal Structure

root_folder/
│
├── lidar/
│   └── Contains raw LIDAR files for the area of interest. These files are the input for further spatial processing via Whitebox Tools: Rooftop Analysis.
│
├── pyqgis_whitebox_tool/
│   └── Stores output from LIDAR processing scripts, run via the WhiteboxTools Rooftop Analysis [is created upon script run].
│
├── raster/
│   └── Holds supporting raster datasets (e.g., land cover, elevation models) used in analysis.
│
├── vector/
│   └── Contains project-related vector files, including:
│       - Building footprints
│
├── LoadProGen/
|    └── Includes electricity load profiles for households. These are used for energy demand analysis within the framework.
|
└── data/
    └── Stores temporary and Python scripting Modules' output data files

🧰 Design Principles

Rather than providing a fully optimized or production-ready software stack, the framework focuses on:

• Accessibility and transparency

• Semi-automated scripting (Python + QGIS)

• Adaptability to public data

• Open-source methodology

It serves as a realistic, reproducible tutorial for researchers, municipalities, energy analysts, and citizen energy enthusiasts interested in:

• Spatial mapping of energy vulnerability

• Estimating socio-technical renovation potential

• Building data pipelines with modest technical capacity

📊 Input Data and Processing

Key parameter definitions and scenario variables are defined in Excel: 📄 00_input_data.xlsx

Intermediate data is exchanged through .xlsx, .csv, and .geojson formats, processed with:

Python libraries: pandas, geopandas, shapely

Open-source GIS software: QGIS (for visual inspection and manual adjustments, file: qgis_project_map) [to be improved]

Module 4 Input File:

The file 00_mod_04_input_data_census_id_ener_consum_profiling.xlsx contains input data used in Module 4. It supports the assignment of dwelling typologies based on socio-demographic census data and includes a custom interpretation of energy consumption profiles per dwelling type.

⚠️ Disclaimer

• This framework is not performance-optimized, nor fully validated for production-grade software engineering. It has been:

• Manually checked during key lifecycle stages: requirements, design, implementation, integration

• Functionally tested only for core component consistency (unit/system-level)

• It aims to demonstrate a semi-automated, spatially extended workflow that is accessible for engineers and enthusiasts not primarily involved in software development, especially with the emergence of AI-powered agents in user workflows. It is intended for high-level scenario testing and mapping, not detailed simulation or modeling.

🔄 Current Limitations and Next Steps

Although the framework covers spatial processing and workflow design, some components remain under development:

Planned Improvements

• ⚙️ Align codebase closer to basic principles of PEP8 and OOP practices

• 📊 Add interactive visualizations as a dashboard-like presentation and output exploration

• 🛢️ Integrate lightweight database functionality (e.g. DuckDB) for smoother data flow