# Electricity Demand Forecasting in Future Grid States: A Digital Twin-Based Simulation Study

arXiv: https://arxiv.org/abs/2503.04757 · HTML: https://mlsanity.com/paper/2503.04757/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 35 references that are in the map.
- First posted on arXiv 10 Feb 2025.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges](https://mlsanity.com/paper/1802.04117/) (2018)
- [Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations](https://mlsanity.com/paper/2106.00006/) (2021)
- [Short-Term Electricity Load Forecasting Using the Temporal Fusion Transformer: Effect of Grid Hierarchies and Data Sources](https://mlsanity.com/paper/2305.10559/) (2023)
- [Digital Twins based Day-ahead Integrated Energy System Scheduling under Load and Renewable Energy Uncertainties](https://mlsanity.com/paper/2109.14423/) (2021)
- [Meta-Regression Analysis of Errors in Short-Term Electricity Load Forecasting](https://mlsanity.com/paper/2305.18550/) (2023)
- [Enhancing the Performance of Multi-Agent Reinforcement Learning for Controlling HVAC Systems](https://mlsanity.com/paper/2309.06940/) (2023)
- [Modeling of Annual and Daily Electricity Demand of Retrofitted Heat Pumps based on Gas Smart Meter Data](https://mlsanity.com/paper/2310.02756/) (2023)

## Built on by (most cited first, 2 of 2)

- [FETS Benchmark: Foundation Models Enable Scalable and Generalizable Energy Time Series Forecasting](https://mlsanity.com/paper/2604.22328/) (2026)
- [Benchmarking Transformer and xLSTM for Time-Series Forecasting of Heat Consumption](https://mlsanity.com/paper/2605.09722/) (2026)

## Abstract (arXiv)

Short-term forecasting of residential electricity demand is an important task for utilities. Yet, many small and medium-sized utilities still use simple forecasting approaches such as Synthesized Load Profiles, which treat residential households similarly and neither account for renewable energy installations nor novel large consumers (e.g., heat pumps, electric vehicles). The effectiveness of such "one-fits-all" approaches in future grid states--where decentral generation and sector coupling increases--are questionable. Our study challenges these forecasting practices and investigates whether Machine Learning (ML) approaches are suited to predict electricity demand in today's and in future grid states. We use real smart meter data from 3,511 households in Germany over 34 months. We extrapolate this data with future grid states (i.e., increased decentral generation and storage) based on a digital twin of a local energy system. Our results show that Long Short-Term Memory (LSTM) approaches outperform SLPs as well as simple benchmark estimators with up to 68.5% lower Root Mean Squared Error for a day-ahead forecast, especially in future grid states. Nevertheless, all prediction approaches perform worse in future grid states. Our findings therefore reinforce the need (a) for utilities and grid operators to employ ML approaches instead of traditional demand prediction methods in future grid states and (b) to prepare current ML methods for future grid states.