INTELLIGENT_D4.1_FOS prediction module (GitHub, with docs) V1 [M6]

This report presents the first prototype of the Prediction Module developed under Work Package 4 of the INTELLIGENT project. As a key part of the Flexibility and Energy Optimisation Service (FOS), the module enables short-term forecasting of household electricity demand and solar PV generation using advanced machine learning models. XGBoost showed the best performance among the evaluated methods. The module supports energy communities in optimising local energy use and participating in energy markets. The codebases are made publicly available to promote reuse and collaboration. Future iterations will include the use of real-world data from pilot sites.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-03
DOI
https://doi.org/10.5281/zenodo.23927999
Primary Topic
Energy Load and Power Forecasting
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

INTELLIGENT_D4.1_FOS prediction module (GitHub, with docs) V1 [M6]

Technical University of Munich (TUM)
Zenodo (CERN European Organization for Nuclear Research)
Energy Load and Power Forecasting
article

INTELLIGENT_D4.1_FOS prediction module (GitHub, with docs) V1 [M6]

Technical University of Munich (TUM)
article en

Abstract

This report presents the first prototype of the Prediction Module developed under Work Package 4 of the INTELLIGENT project. As a key part of the Flexibility and Energy Optimisation Service (FOS), the module enables short-term forecasting of household electricity demand and solar PV generation using advanced machine learning models. XGBoost showed the best performance among the evaluated methods. The module supports energy communities in optimising local energy use and participating in energy markets. The codebases are made publicly available to promote reuse and collaboration. Future iterations will include the use of real-world data from pilot sites.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 26%
Energy Load and Power Forecasting
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon
INTELLIGENT_D4.1_FOS prediction module (GitHub, with docs) V1 [M6] — Technical University of Munich (TUM) · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS