# Understanding electricity prices beyond the merit order principle using explainable AI

arXiv: https://arxiv.org/abs/2212.04805 · HTML: https://mlsanity.com/paper/2212.04805/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 58 references that are in the map.
- First posted on arXiv 9 Dec 2022.

## Builds on (most cited first, 11 of 11)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Consistent Individualized Feature Attribution for Tree Ensembles](https://mlsanity.com/paper/1802.03888/) (2018)
- [Towards Causal Representation Learning](https://mlsanity.com/paper/2102.11107/) (2021)
- [Explainable Machine Learning for Scientific Insights and Discoveries](https://mlsanity.com/paper/1905.08883/) (2019)
- [Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark](https://mlsanity.com/paper/2008.08004/) (2020)
- [Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models](https://mlsanity.com/paper/2011.01625/) (2020)
- [Revealing drivers and risks for power grid frequency stability with explainable AI](https://mlsanity.com/paper/2106.04341/) (2021)
- [An Interpretable Probabilistic Model for Short-Term Solar Power Forecasting Using Natural Gradient Boosting](https://mlsanity.com/paper/2108.04058/) (2021)
- [Secondary control activation analysed and predicted with explainable AI](https://mlsanity.com/paper/2109.04802/) (2021)

## Built on by (most cited first, 6 of 6)

- [Probabilistic Forecasting of Day-Ahead Electricity Prices and their Volatility with LSTMs](https://mlsanity.com/paper/2310.03339/) (2023)
- [Multivariate Scenario Generation of Day-Ahead Electricity Prices using Normalizing Flows](https://mlsanity.com/paper/2311.14033/) (2023)
- [Explaining deep neural network models for electricity price forecasting with XAI](https://mlsanity.com/paper/2506.19894/) (2025)
- [Explainable time-series forecasting with sampling-free SHAP for Transformers](https://mlsanity.com/paper/2512.20514/) (2025)
- [Analysing drivers and interdependencies in European electricity markets using XAI](https://mlsanity.com/paper/2606.19118/) (2026)
- [Does Demand Response Increase Vulnerability to Cyber Attacks by Adversarial Data Modifications?](https://mlsanity.com/paper/2607.06632/) (2026)

## Abstract (arXiv)

Electricity prices in liberalized markets are determined by the supply and demand for electric power, which are in turn driven by various external influences that vary strongly in time. In perfect competition, the merit order principle describes that dispatchable power plants enter the market in the order of their marginal costs to meet the residual load, i.e. the difference of load and renewable generation. Many market models implement this principle to predict electricity prices but typically require certain assumptions and simplifications. In this article, we present an explainable machine learning model for the prices on the German day-ahead market, which substantially outperforms a benchmark model based on the merit order principle. Our model is designed for the ex-post analysis of prices and thus builds on various external features. Using Shapley Additive exPlanation (SHAP) values, we can disentangle the role of the different features and quantify their importance from empiric data. Load, wind and solar generation are most important, as expected, but wind power appears to affect prices stronger than solar power does. Fuel prices also rank highly and show nontrivial dependencies, including strong interactions with other features revealed by a SHAP interaction analysis. Large generation ramps are correlated with high prices, again with strong feature interactions, due to the limited flexibility of nuclear and lignite plants. Our results further contribute to model development by providing quantitative insights directly from data.