Multi-year Long-term Load Forecast for Area Distribution Feeders based on Selective Sequence Learning

TL;DR

This paper introduces an advanced multi-year load forecasting method for distribution feeders that combines sequence learning, unsupervised grouping, and selective configuration to improve accuracy over traditional approaches.

Contribution

It extends previous sequence prediction models to multi-year forecasts, incorporating unsupervised feeder grouping and a novel selective learning mechanism with GRU networks.

Findings

Achieved superior multi-year load forecast accuracy in real urban systems.

Demonstrated the effectiveness of selective sequence learning for individual feeders.

Outperformed traditional and previous methods in real-world tests.

Abstract

Long-term load forecast (LTLF) for area distribution feeders is one of the most critical tasks frequently performed in electric distribution utility companies. For a specific planning area, cost-effective system upgrades can only be planned out based on accurate feeder LTLF results. In our previous research, we established a unique sequence prediction method which has the tremendous advantage of combining area top-down, feeder bottom-up and multi-year historical data all together for forecast and achieved a superior performance over various traditional methods by real-world tests. However, the previous method only focused on the forecast of the next one-year. In our current work, we significantly improved this method: the forecast can now be extended to a multi-year forecast window in the future; unsupervised learning techniques are used to group feeders by their load composition features to improve accuracy; we also propose a novel selective sequence learning mechanism which uses Gated Recurrent Unit network to not only learn how to predict sequence values but also learn to select the best-performing sequential configuration for each individual feeder. The proposed method was tested on an actual urban distribution system in West Canada. It was compared with traditional methods and our previous sequence prediction method. It demonstrates the best forecasting performance as well as the possibility of using sequence prediction models for multi-year component-level load forecast.