# Predicting the Voltage Distribution for Low Voltage Networks using Deep Learning

## TL;DR

This paper proposes a deep learning approach to predict voltage distribution in low-voltage networks using partial smart meter data, addressing data availability issues and enhancing network management amid increasing renewable and electric vehicle integration.

## Contribution

It introduces a neural network model that accurately predicts voltage distribution with limited smart meter data, reducing the need for full observability.

## Key findings

- Smart meter data from key locations suffices for accurate voltage prediction.
- Deep learning models outperform traditional methods with partial data.
- Effective voltage distribution prediction is achievable without full network observability.

## Abstract

The energy landscape for the Low-Voltage (LV) networks are beginning to change; changes resulted from the increase penetration of renewables and/or the predicted increase of electric vehicles charging at home. The previously passive `fit-and-forget' approach to LV network management will be inefficient to ensure its effective operations. A more adaptive approach is required that includes the prediction of risk and capacity of the circuits. Many of the proposed methods require full observability of the networks, motivating the installations of smart meters and advance metering infrastructure in many countries. However, the expectation of `perfect data' is unrealistic in operational reality. Smart meter (SM) roll-out can have its issues, which may resulted in low-likelihood of full SM coverage for all LV networks. This, together with privacy requirements that limit the availability of high granularity demand power data have resulted in the low uptake of many of the presented methods. To address this issue, Deep Learning Neural Network is proposed to predict the voltage distribution with partial SM coverage. The results show that SM measurements from key locations are sufficient for effective prediction of voltage distribution.