A Hybrid Distribution Feeder Long-Term Load Forecasting Method Based on Sequence Prediction

TL;DR

This paper introduces a hybrid long-term load forecasting method for distribution feeders using sequence prediction models like LSTM and GRU, effectively integrating multi-level information for improved accuracy.

Contribution

It presents a novel hybrid approach combining top-down, bottom-up, and sequence data, utilizing advanced neural networks for enhanced long-term load forecasting.

Findings

LSTM and GRU outperform traditional models in accuracy.

The hybrid method effectively integrates multi-level information.

Application to a large urban grid demonstrates practical benefits.

Abstract

Distribution feeder long-term load forecast (LTLF) is a critical task many electric utility companies perform on an annual basis. The goal of this task is to forecast the annual load of distribution feeders. The previous top-down and bottom-up LTLF methods are unable to incorporate different levels of information. This paper proposes a hybrid modeling method using sequence prediction for this classic and important task. The proposed method can seamlessly integrate top-down, bottom-up and sequential information hidden in multi-year data. Two advanced sequence prediction models Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks are investigated in this paper. They successfully solve the vanishing and exploding gradient problems a standard recurrent neural network has. This paper firstly explains the theories of LSTM and GRU networks and then discusses the steps of feature selection, feature engineering and model implementation in detail. In the end, a real-world application example for a large urban grid in West Canada is provided. LSTM and GRU networks under different sequential configurations and traditional models including bottom-up, ARIMA and feed-forward neural network are all implemented and compared in detail. The proposed method demonstrates superior performance and great practicality.