# Privacy Preserving Demand Forecasting to Encourage Consumer Acceptance of Smart Energy Meters

arXiv: https://arxiv.org/abs/2012.07449 · HTML: https://mlsanity.com/paper/2012.07449/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2020.

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

- [Privacy-Preserving Explainable AIoT Application via SHAP Entropy Regularization](https://mlsanity.com/paper/2511.09775/) (2025)
- [IRMAC: Interpretable Refined Motifs in Binary Classification for Smart Grid Applications](https://mlsanity.com/paper/2109.13732/) (2021)

## Abstract (arXiv)

In this proposal paper we highlight the need for privacy preserving energy demand forecasting to allay a major concern consumers have about smart meter installations. High resolution smart meter data can expose many private aspects of a consumer's household such as occupancy, habits and individual appliance usage. Yet smart metering infrastructure has the potential to vastly reduce carbon emissions from the energy sector through improved operating efficiencies. We propose the application of a distributed machine learning setting known as federated learning for energy demand forecasting at various scales to make load prediction possible whilst retaining the privacy of consumers' raw energy consumption data.