Data Bias Risks in Energy Forecasting Models

1. Introduction

Data bias in energy forecasting models means that the data used to train a forecasting system is not properly representative of the real electricity system. As a result, the model may repeatedly produce inaccurate forecasts.

Energy forecasting is used to predict electricity demand, renewable generation, electricity prices, congestion and system requirements. Today, many forecasting systems use artificial intelligence and machine learning. Recent research shows that energy datasets can be incomplete, noisy or biased, which can reduce the ability of models to work correctly in new situations. (DOI)

2. Meaning of Data Bias

A forecasting model learns from historical information. If the historical data contains a systematic problem, the model may learn that problem.

For example, suppose a model is trained mainly using data from normal weather conditions. During an extreme heatwave, electricity demand may become much higher than the model expects. The forecast can therefore be wrong.

Bias may arise from:

incomplete historical data;

incorrect meter readings;

missing information;

over-representation of some consumer groups;

limited geographical coverage;

unusual weather events;

changes in consumer behaviour; and

rapid growth of electric vehicles or renewable generation.

3. Types of Bias

Historical bias: The model depends heavily on past patterns that may no longer represent the electricity system.

Sampling bias: Some areas or consumer groups may have much more data than others.

Measurement bias: Faulty meters or inconsistent measurement methods can enter the training dataset.

Temporal bias: Old data may not reflect current conditions. For example, electricity demand after large-scale EV adoption may differ significantly from historical demand.

Geographical bias: A model trained using data from one region may perform poorly in another region because weather, industry and consumer behaviour are different.

4. Effect on Electricity Systems

Biased forecasting can create serious operational problems.

If demand is underestimated, system operators may not arrange enough generation, storage or balancing resources. This can increase system stress.

If demand is overestimated, unnecessary generation or reserve capacity may be arranged, increasing costs.

Bias can also affect renewable-energy forecasting. Solar and wind generation depend heavily on weather conditions, and historical data may not properly represent unusual weather patterns.

Research on PJM electricity-load forecasting has specifically examined model bias and error propagation, showing why correcting systematic forecasting errors is important for large power systems. (IDEAS/RePEc)

5. Bias and Energy Transition

The electricity system is changing quickly because of:

solar and wind power;

battery storage;

electric vehicles;

heat pumps;

demand-response programmes; and

distributed generation.

A model trained mainly on older electricity patterns may therefore become less reliable. Recent EU policy work also notes that energy AI development is affected by limited access to high-quality real-world datasets and by data that may be incomplete or inconsistent. (Eur-Lex)

6. Legal Importance

Data bias creates legal concerns about accuracy, transparency, accountability and fair decision-making.

If a forecasting model is used to make important decisions, the organisation using it should understand the quality and limitations of the underlying data. Data-protection law is particularly relevant when forecasting models process information connected to identifiable consumers.

The GDPR requires personal data to be processed according to principles including accuracy and fairness. Therefore, inaccurate consumer data should not simply be accepted because it is convenient for an algorithm.

7. Relevant Case Laws

SCHUFA Holding (Scoring), Case C-634/21

The CJEU considered automated scoring under Article 22 GDPR. The Court examined when automated creation and use of a probability score can amount to automated decision-making. (InfoCuria)

The case is relevant to energy forecasting because it demonstrates that automated systems using personal information may require legal safeguards where their outputs affect individuals. Energy companies using consumer-level forecasting or automated profiling should therefore consider transparency and accountability.

SCHUFA Holding, Joined Cases C-26/22 and C-64/22

These cases also concerned personal-data processing and demonstrate the importance of lawful and properly controlled use of personal information in automated systems. (curia)

These are not electricity cases, but their data-governance principles can be applied where energy forecasting involves personal consumer data.

8. Preventing Data Bias

Energy companies and system operators can reduce bias by:

regularly checking training datasets;

including different geographical areas and consumer groups;

updating old datasets;

testing models during unusual weather conditions;

comparing AI forecasts with traditional forecasting methods;

monitoring forecasting errors continuously; and

keeping human oversight over important decisions.

Models should also be tested using new and independent data, rather than only the data on which they were trained.

9. Conclusion

Data bias is an important legal and technical risk in modern energy forecasting. A forecasting model is only as reliable as the data used to build and test it. Incomplete, outdated or unbalanced data can produce systematically wrong predictions, affecting electricity generation, grid planning, prices, balancing and renewable-energy integration.

Therefore, energy-law frameworks should encourage data accuracy, transparency, regular model testing, accountability and appropriate human oversight. The aim is not simply to use more AI, but to ensure that forecasting systems are based on representative and reliable data and that their limitations are properly understood.