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Data Science Final Year Topic: Evaluation of Electricity Demand Baseline Forecasts

This Data Science final year project investigates comparing simple and complex forecasts for short electricity-load series, with explicit data definitions and reproducible analysis rather than invented findings.

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Why choose this project topic?

This topic turns comparing simple and complex forecasts for short electricity-load series into a measurable analytical question. Comparing absolute error, peak-period error and forecast stability gives a student material for visual results, methodological criticism and a discussion of how the data's limitations change the conclusion.

How does electricity demand baseline forecasts affect comparing simple and complex forecasts for short electricity-load series, as measured by absolute error, peak-period error and forecast stability?

Choose an accessible licensed or synthetic dataset, agree the unit of analysis and narrow comparing simple and complex forecasts for short electricity-load series to a reproducible comparison your supervisor can review.

Proposed project objectives

  1. 01Define the data, assumptions and comparison design for comparing simple and complex forecasts for short electricity-load series.
  2. 02Use licensed aggregate demand observations, establish seasonal-naive baselines and evaluate models with rolling splits and missing-meter scenarios.
  3. 03Compare absolute error, peak-period error and forecast stability and report uncertainty and sensitivity to analytical choices.

A suggested research approach

Use licensed aggregate demand observations, establish seasonal-naive baselines and evaluate models with rolling splits and missing-meter scenarios. Confirm the data licence and variable definitions before analysis. Preserve an untouched evaluation set where relevant, document exclusions and missingness, and report absolute error, peak-period error and forecast stability with uncertainty or sensitivity checks rather than selecting only favourable results.

What you will need

  • Licensed aggregate load series
  • Forecasting library
  • Calendar feature definitions

Keep your project scope clear

Results do not guarantee supply reliability or forecast demand outside the observed system.

Data Science project chapter outline

Use this outline as a starting point. You can edit the chapter titles to match your department’s format during setup.

  1. Chapter 1Introduction
  2. Chapter 2Literature Review
  3. Chapter 3Research Methodology
  4. Chapter 4Presentation and Analysis of Results
  5. Chapter 5Summary, Conclusion and Recommendations

Turn this topic into your own final year project.

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