# Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations

arXiv: https://arxiv.org/abs/2106.00006 · HTML: https://mlsanity.com/paper/2106.00006/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 247 references that are in the map.
- First posted on arXiv 30 May 2021.

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

- [Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges](https://mlsanity.com/paper/1802.04117/) (2018)
- [Deep Learning for Time Series Forecasting: The Electric Load Case](https://mlsanity.com/paper/1907.09207/) (2019)
- [A Time-Series Distribution Test System Based on Real Utility Data](https://mlsanity.com/paper/1906.04078/) (2019)
- [Lasso estimation for GEFCom2014 probabilistic electric load forecasting](https://mlsanity.com/paper/1603.01376/) (2016)
- [Shape-Based Approach to Household Load Curve Clustering and Prediction](https://mlsanity.com/paper/1702.01414/) (2017)
- [Electricity Demand Forecasting by Multi-Task Learning](https://mlsanity.com/paper/1512.08178/) (2015)
- [Achieving the Dispatchability of Distribution Feeders through Prosumers Data Driven Forecasting and Model Predictive Control of Electrochemical Storage](https://mlsanity.com/paper/1602.02265/) (2016)
- [Big Data Analytics for Dynamic Energy Management in Smart Grids](https://mlsanity.com/paper/1504.02424/) (2015)
- [Hierarchical Demand Forecasting Benchmark for the Distribution Grid](https://mlsanity.com/paper/1910.03976/) (2019)
- [A comparison of non-intrusive load monitoring methods for commercial and residential buildings](https://mlsanity.com/paper/1408.6595/) (2014)
- [Local Short Term Electricity Load Forecasting: Automatic Approaches](https://mlsanity.com/paper/1702.08025/) (2017)

## Built on by (most cited first, 17 of 17)

- [Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flows](https://mlsanity.com/paper/2204.13939/) (2022)
- [Transformer Training Strategies for Forecasting Multiple Load Time Series](https://mlsanity.com/paper/2306.10891/) (2023)
- [Short-Term Electricity-Load Forecasting by Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2408.16202/) (2024)
- [Explainable Load Forecasting with Covariate-Informed Time Series Foundation Models](https://mlsanity.com/paper/2604.28149/) (2026)
- [Transfer Learning in Transformer-Based Demand Forecasting For Home Energy Management System](https://mlsanity.com/paper/2310.19159/) (2023)
- [Defining 'Good': Evaluation Framework for Synthetic Smart Meter Data](https://mlsanity.com/paper/2407.11785/) (2024)
- [Electricity Demand Forecasting in Future Grid States: A Digital Twin-Based Simulation Study](https://mlsanity.com/paper/2503.04757/) (2025)
- [Explainable time-series forecasting with sampling-free SHAP for Transformers](https://mlsanity.com/paper/2512.20514/) (2025)
- [Time Series Foundation Models for Energy Load Forecasting on Consumer Hardware: A Multi-Dimensional Zero-Shot Benchmark](https://mlsanity.com/paper/2602.10848/) (2026)
- [A Global Modeling Approach for Load Forecasting in Distribution Networks](https://mlsanity.com/paper/2204.00493/) (2022)
- [Meta-Regression Analysis of Errors in Short-Term Electricity Load Forecasting](https://mlsanity.com/paper/2305.18550/) (2023)
- [One Model to Forecast Them All and in Entity Distributions Bind Them](https://mlsanity.com/paper/2501.15499/) (2025)
- [Bayesian Transformer for Probabilistic Load Forecasting in Smart Grids](https://mlsanity.com/paper/2603.07899/) (2026)
- [A Benchmark for Electrical Load Forecasting Across Grid Levels: Time-Series Transformers Outperform Established Methods](https://mlsanity.com/paper/2607.15705/) (2026)
- [Generating peak-aware pseudo-measurements for low-voltage feeders using metadata of distribution system operators](https://mlsanity.com/paper/2409.19713/) (2024)
- [Coherent Load Profile Synthesis with Conditional Diffusion for LV Distribution Network Scenario Generation](https://mlsanity.com/paper/2510.12832/) (2025)
- [Extending Load Forecasting from Zonal Aggregates to Individual Nodes for Transmission System Operators](https://mlsanity.com/paper/2510.14983/) (2025)

## Abstract (arXiv)

The increased digitalisation and monitoring of the energy system opens up numerous opportunities to decarbonise the energy system. Applications on low voltage, local networks, such as community energy markets and smart storage will facilitate decarbonisation, but they will require advanced control and management. Reliable forecasting will be a necessary component of many of these systems to anticipate key features and uncertainties. Despite this urgent need, there has not yet been an extensive investigation into the current state-of-the-art of low voltage level forecasts, other than at the smart meter level. This paper aims to provide a comprehensive overview of the landscape, current approaches, core applications, challenges and recommendations. Another aim of this paper is to facilitate the continued improvement and advancement in this area. To this end, the paper also surveys some of the most relevant and promising trends. It establishes an open, community-driven list of the known low voltage level open datasets to encourage further research and development.