Energiforsk 2026-1168 AI-modeller Prognostisering Efterfrågan El (2026)

Full citation: Sätfeld, L.R. & Popoff, M. (RISE Research Institutes of Sweden). AI-modeller för prognostisering av efterfrågan på el. Energiforsk Report 2026:1168. Stockholm: Energiforsk AB, 2026. Program: Elnätens hållbara teknikutveckling och digitalisering. Sub-report 1 of the PREDATOR project (Predictions for Data-driven Decision Support).

Corrected 2026-08-17: the original ingest recorded the authors as “Sütfeld, L. & Popoff, A.” and PREDATOR’s expansion as “Predicting Energy demand and forecasting changes in local grid loads” — both wrong against the primary document, which gives the authors as Leon René Sätfeld & Michael Popoff and PREDATOR as “Predictions for Data-driven Decision Support.” Workshop 3 is also corrected below (was dated March 2024; the report gives 21 February 2024).

Open access: Published by Energiforsk. RISE Research Institutes of Sweden is Sweden’s largest research and technology organization (statligt forskningsinstitut).

Summary

This is the stakeholder needs analysis report for the PREDATOR project — a participatory investigation into what Swedish DSOs actually need from AI and data-driven load forecasting tools. Through four workshops with eight Swedish DSOs (November 2023 – April 2024), the report documents the current state of DSO demand forecasting capabilities, identifies key gaps and requirements, and proposes an “analyze-aggregate-extrapolate” framework as a conceptual architecture for a holistic forecasting solution.

The report’s primary finding is stark: many Swedish DSOs lack internal data-driven demand/load forecasting models entirely, and those that have some capability do not have integrated end-to-end solutions. DSOs want holistic tools — not partial components — and the barriers to adoption are as much organizational (internal data management capacity) as technical.

Sub-report 2 (planned) will expand the framework with survey data (Eurobarometer, ESS, SCB) and economic indicators for the extrapolation step. A larger follow-up project, DESGRID, is planned based on the PREDATOR findings.

Key claims

Workshop design

• 4 workshops: 29 November 2023, 17 January 2024, 21 February 2024, 17 April 2024
• 8 participating DSOs:
  • Ellevio (large; ~1M customers; national coverage)
  • Göteborg Energi (large; Gothenburg)
  • Mölndal Energi (medium; Mölndal, co-operator of Effekthandel Väst)
  • Trollhättan Energi (medium; Trollhättan)
  • Jönköping Energi (medium; Jönköping, participated in PREDATOR workshops and SWITCH)
  • Umeå Energi (medium; Umeå)
  • C+ Energi (small-medium)
  • Karlstads Kommun (municipal; Karlstad)

This sample covers DSOs from three of Sweden’s four electricity areas (SE3/SE4 dominated; Umeå suggests SE2) and spans from very large to small-municipal scale — a reasonably representative cross-section of Swedish DSO diversity.

Core finding: DSOs want holistic solutions

The primary insight from workshops: DSOs request complete, integrated forecasting solutions, not partial or specialized tools. Specifically:

• DSOs do not want a tool that only processes one data type, covers one load category, or handles one step in the forecasting workflow
• DSOs need help with the entire chain: from data management through model building through interpretation and planning integration
• Partial tools create integration burden that DSOs — especially smaller ones — cannot absorb
• This finding directly mirrors Source - Energiforsk 2026-1157 Nationell Metod Effekt och Kapacitetsprognoser (2026)‘s finding that 28% of DSOs lack documented methodology: the demand is not for more sophisticated components but for more accessible complete solutions

Underlying cause: per the report, many DSOs lack the internal, established infrastructure to integrate predictions of high-load technologies (EVs, heat pumps) into planning — not just a more sophisticated model. The organizational/infrastructure barrier is presented as at least as significant as the technical one.

Current forecasting gap

• Many of the 8 participating DSOs have no internal data-driven demand/load forecasting models at all — the report notes this means DSOs typically lack the infrastructure to integrate predictions of high-load technologies (EVs, heat pumps) even where forecasting exists elsewhere in the organization (“…vilket innebär att det i många fall saknas en färdig infrastruktur för att integrera prediktioner av högbelastande laster”)
• Even DSOs that have some quantitative forecasting do not have integrated models that combine:
  • Current DER inventory (EVs, solar PV, heat pumps, batteries)
  • Behavioral and socioeconomic drivers
  • Future growth trajectories

Proposed “analyze-aggregate-extrapolate” framework

The report proposes a three-step conceptual architecture for holistic DSO demand forecasting:

Step 3 data sources (planned for Sub-report 2): the report lists 17 candidate survey/statistics sources for consideration — including SCB (Statistics Sweden), Riksbanken, Skatteverket, Eurobarometer, Eurostat, ESS (European Social Survey), and the World Values Survey — plus 14 candidate economic indicators (household income, inflation, interest rates, employment, housing costs, energy prices, etc.), with particular interest in data available at the municipality level. The report does not attribute specific indicators to specific sources; these are candidates to be narrowed down, not a finalized data plan.

DESGRID — planned follow-up project

Based on PREDATOR Sub-report 1 findings, a larger follow-up project DESGRID is planned. DESGRID would implement and validate the analyze-aggregate-extrapolate framework as a concrete tool for Swedish DSOs. Scope and timeline at time of this report’s publication: under development.

Corrected 2026-08-17: the original ingest attributed the funding delay to “lower review committee availability” for summer submissions. The report gives a different, more specific mechanism: a Vinnova Advanced Digitalization call (deadline summer 2024) required 50% in-kind co-funding (staff time) from participating DSOs, and the short window between the call’s deadline and Swedish summer holidays meant several interested DSOs could not confirm their commitments in time — which would have under-funded the project relative to plan. The application was postponed rather than submitted incomplete, with a better-timed retry planned for 2025.

Relationship to other forecasting initiatives

This project addresses a different aspect of the forecasting challenge than Source - Energiforsk 2026-1157 Nationell Metod Effekt och Kapacitetsprognoser (2026):

Both projects identify the same underlying problem (inadequate DSO forecasting capability) and are complementary solutions at different levels of the system.

The “identify current EV/solar/HP inventory” step in PREDATOR’s “analyze” phase directly corresponds to the RISE AMI classification tool already mentioned in DSO Flexibility Need Calculation Methods › Practical tools for flexibility need quantification — confirming that RISE was developing this capability simultaneously in multiple research programs.

Relevance to the wiki

This source directly informs or strengthens:

• Distribution System Operator — documents the practical AI/data readiness gap at Swedish DSOs; confirms that the lack of data-driven forecasting is a known sector-wide issue, not just a few outliers; the analyze-aggregate-extrapolate framework as a structured conceptual approach to closing the gap
• Flexibility Need Assessment — the “analyze” step (identify current DER inventory) is a prerequisite for accurate flexibility need quantification; PREDATOR addresses the same data foundation gap that the Endre probabilistic tool and RISE AMI classification tool address
• Distribution Network Development Plan — better DER-level load forecasting directly feeds DNDP scenario development (Pillar 1 of the three-pillar planning process); PREDATOR Sub-report 2 data sources (ESS, SCB, Eurobarometer) could provide inputs to DNDP behavioral scenario development
• Aggregation — the “aggregate” step (combining DER inventory into feeder-level profiles) is exactly what Virtual Power Plant and aggregation platforms do from a commercial standpoint; shared research interest in aggregation methods

Internal links to other Energiforsk 2024–2026 program sources:

• Source - Energiforsk 2024-1043 DNDP Analys och Flexibilitet (2024) — the automation index ranks “current DER inventory identification” as high priority; PREDATOR’s “analyze” step is the automation of that sub-task
• Source - Energiforsk 2026-1157 Nationell Metod Effekt och Kapacitetsprognoser (2026) — complementary approach (top-down vs bottom-up); same problem, different level of resolution; both identify that the sector’s forecasting capability is inadequate
• Source - Energiforsk 2026-1151 Effektauktioner med Värmepumpar (2026) — both require accurate per-unit DER profiles; the SVM normalization used in 2026-1151 is a related methodology to PREDATOR’s extrapolation step

Data gaps

• Sub-report 1 is a needs analysis only — no implemented model, validated result, or DSO test deployment
• DESGRID is planned but not funded as of this report’s publication; outcome uncertain
• The 8 participating DSOs are a self-selected group interested in the topic — may not represent the ~120 smaller Swedish DSOs with lower digital maturity
• The Vinnova application failure (summer scheduling) introduces a research continuity risk — if DESGRID funding is not secured, the analyze-aggregate-extrapolate framework remains conceptual