Load Forecasting

Predicting future electricity demand, used at every layer of the power system — from real-time grid control to 30-year network investment planning. The forecast horizon determines which decisions can be made; the method determines whether those decisions are reliable. Without load forecasting, neither market participants nor grid operators can act efficiently.

Why forecasting matters for flexibility markets

Load forecasting is the foundational data layer for operational flexibility market participation. It appears at every stage of the flexibility service chain:

• DSO day-ahead procurement — DSOs procure flexibility based on a forecast that a specific grid segment will exceed its limit during a specific window. In CoordiNet, SWITCH, sthlmflex, and all operational Swedish LFMs, the procurement call is triggered by the DSO’s local load forecast. Without it, the DSO cannot know when to buy or how much. For availability-based products (LFM-h/p), the DSO must forecast which hours are congestion-risk in order to define the activation window in the product. DSO forecasting is therefore what turns a grid need into a market order.
• BRP plan submission — Balance Responsible Parties must submit consumption forecasts to Svenska kraftnät the day ahead; forecast error drives imbalance settlement charges under the Nordic enpris settlement model
• FSP bidding — Flexibility Service Providers cannot construct a bid (quantity, activation window) without knowing baseline consumption; the bid is the difference between baseline and committed curtailment
• DR scheduling — aggregators must predict when resources are available to respond; heat pump flexibility depends on weather/thermal state; EV flexibility depends on connection and departure time
• DSO capacity planning — grid operators must forecast local peak demand to decide whether to procure flexibility or reinforce the grid; DNDP load scenarios are built on MTLF/LTLF models

This places load forecasting as a prerequisite for the entire explicit flexibility market architecture on both sides of the trade — not a technical detail, but an enabling condition. Unknown demand = no market. (Source - Load Forecasting Methods Survey (2025))

But good STLF for a flexibility market is not the same as low MAPE: the forecast is a financial instrument defined by the market’s settlement rules, so “good” means accurate where errors are expensive, manipulation-resistant where money is at stake, and current at gate closure. For the full argument and the achievability levers, see STLF for Flexibility Markets — What Counts as Good and How to Achieve It.

Four forecasting horizons

The STLF tier is the operational market participation layer. MTLF and LTLF are used in Swedish regulation primarily for DNDP and FNA processes — the domain of the Energiforsk national methodology work (Source - Energiforsk 2026-1157 Nationell Metod Effekt och Kapacitetsprognoser (2026)).

Methods overview

Statistical methods

ARIMA/SARIMA (time-series; ARIMAX adds exogenous variables such as weather and time-of-day) are standard baselines for day-ahead STLF and MTLF. Multiple regression models handle stable medium-term patterns. All statistical methods have limited capacity for non-linear dynamics.

Machine learning

ANN, SVM/SVR, Random Forest, and XGBoost handle non-linearity and extract feature importance. More training data required than statistical methods. XGBoost is widely used in industry STLF due to speed and feature interpretability.

Deep learning

LSTM (Long Short-Term Memory) is the most studied DL method for STLF — its architecture captures long-range temporal dependencies naturally. CNN extracts local temporal patterns faster. BiLSTM captures bidirectional temporal context. Transformer models use attention mechanisms for non-sequential patterns.

Hybrid models — consistently best results

Combining architectures reliably outperforms any single model. CNN-LSTM, CNN-GRU, CNN-BiLSTM (Bayesian-optimized), and LSTM-Transformer are the most documented combinations. Quantified example: LSTM-BPNN achieved 1.47% MAPE vs 3.55% for standalone LSTM and 4.09% for standalone backpropagation network in a comparative study. (Source - Load Forecasting Methods Survey (2025))

Key input variables

Temperature is the dominant driver at all timescales — heating and cooling demands track outdoor temperature. Secondary inputs: historical load (capturing behavioral patterns), time features (hour-of-day, day-of-week, season, holidays), and economic activity indicators for MTLF/LTLF.

EV penetration and DR participation are increasingly important inputs as both reshape load patterns in ways that historical training data does not represent. An EV charging session adds 7–22 kW of local demand in patterns with no equivalent in pre-EV data.

Performance metrics

MAPE is the standard comparative metric across the literature. Hybrid models consistently achieve lower MAPE than single-method models across comparative studies.

Forecasting as a constraint on market design

The BRP imbalance channel

BRPs bear the financial cost of forecast error in the Nordic imbalance settlement framework. Poor STLF translates directly to imbalance charges. As the BSP/BRP framework extends to aggregated distributed resources (households, EVs, batteries), accuracy requirements cascade from large generators down to household resource pools.

The baseline problem

Explicit DR bids require a verified baseline — the counterfactual consumption absent flexibility activation. Baseline methodology is one of the most contested elements of Network Code on Demand Response design: an inaccurate baseline either inflates claimed DR delivery or fails to capture real curtailment. This makes load forecasting not just an operational input but an element of market integrity. See Baseline Methods for the full treatment.

Implicit DR synchronization at scale

When many BRPs respond to the same price signal with similar forecasting models, aggregate forecast errors can synchronize — amplifying system imbalances. This structural tension between implicit flexibility (the EU’s preferred pathway) and system operability is documented in Demand Response › Implicit DR risk at scale.

Data challenges in flexibility contexts

Smart meter data quality: granular AMI data enables building- and device-level STLF, supporting real-time disaggregation and DR dispatch. But noise, missing values, and privacy constraints complicate model training. Low-voltage and microgrid forecasting has higher relative variability than bulk-system forecasting.

EV and DER data gaps: fleet composition, charging behavior, and V2G participation rates are inputs to EV load forecasting that are not yet available at the granularity needed for local grid-level planning.

Historical data invalidation: as load composition changes (EVs replacing ICE, heat pumps replacing gas boilers, large industrial electrification), historical patterns lose predictive value. Models trained on pre-2020 data will underperform for 2030 systems.

Swedish context

Sweden’s national DNDP methodology for load forecasting is developed by Energiforsk, with the most recent formalization in Source - Energiforsk 2026-1157 Nationell Metod Effekt och Kapacitetsprognoser (2026). This covers the LTLF/MTLF layer — capacity and peak demand projections used in DNDP and FNA. The STLF operational layer (day-ahead BRP and FSP market participation) is less standardized and remains a commercial and technical competency developed by individual aggregators, BRPs, and energy service providers.

The DHV data architecture and FIS infrastructure under Network Code on Demand Response will eventually provide the standardized metering and settlement data that makes automated STLF-based DR participation scalable. Until then, the data access challenge remains a structural barrier to household-level STLF.

Data gaps

• DSO-side STLF practice in Swedish LFMs — how CoordiNet, sthlmflex, SWITCH, and Effekthandel Väst DSOs operationally generate the local load forecast that triggers a day-ahead procurement call; what models, what input data, what forecast horizon
• FSP/aggregator STLF practice — what methods are deployed in practice for SWITCH, NODES, and Effekthandel Väst market participation
• Benchmarking data: MAPE achieved at grid-segment level (DSO side) and aggregated-household level (FSP side) in Swedish deployments

Related pages

• Demand Response — what STLF enables: explicit DR participation, FSP bidding, aggregator dispatch
• Balancing Markets — BRP planning and imbalance settlement; reserve procurement that DR participates in
• Flexibility Market — FSP/BRP bidding; baseline methodology as product design challenge
• Baseline Methods — dedicated page on baseline measurement and verification
• Distribution Network Development Plan — DNDP load scenarios built on LTLF/MTLF
• Flexibility Need Assessment — FNA flexibility needs quantification uses LTLF scenarios
• Network Code on Demand Response — baseline methodology regulation; SP qualification requirements