Short-Term Load Forecasting Using an Attended Sequential Encoder-Stacked Decoder Model with Online Training

Sylwia Henselmeyer,Marcin Grzegorzek

doi:10.3390/app11114927

Sylwia Henselmeyer, Marcin Grzegorzek

Open Access

https://doi.org/10.3390/app11114927

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Journal: Applied Sciences	Publication Date: May 27, 2021
Citations: 8	License type: CC BY 4.0

Affiliation: University of Lübeck, Siemens (Germany)

Abstract

The paper presents a new approach for the prediction of load active power 24 h ahead using an attended sequential encoder and stacked decoder model with Long Short-Term Memory cells. The load data are owned by the New York Independent System Operator (NYISO) and is dated from the years 2014–2017. Due to dynamics in the load patterns, multiple short pieces of training on pre-filtered data are executed in combination with the transfer learning concept. The evaluation is done by direct comparison with the results of the NYISO forecast and additionally under consideration of several benchmark methods. The results in terms of the Mean Absolute Percentage Error range from 1.5% for the highly loaded New York City zone to 3% for the Mohawk Valley zone with rather small load consumption. The execution time of a day ahead forecast including the training on a personal computer without GPU accounts to 10 s on average.

Highlights

Load forecasts are substantial in several areas of power network operation independently of the voltage level
The evaluation of the approach has been executed on the data from 2017 and it includes all New York Independent System Operator (NYISO) zones
New York City is the zone with the highest number of residents

Summary

Introduction

Load forecasts are substantial in several areas of power network operation independently of the voltage level. With the increasing number of renewables and more volatility and dynamics in the network, the task of load forecasting becomes even more important. Errors in forecasts have direct financial consequences on the utilities and in the long-term on their customers. They may lead to an inexcusable waste of the green power in the case it has to be curtailed due to network congestions. Short-term load forecasts are used to guarantee a safe and optimal real-time network operation (prevention of network violations, unit commitment, and economic dispatch). Mid-term load forecasts are more important for planning maintenance tasks, load redispatch, and securing a balanced load and generation. Long-term forecasts are mainly relevant for network reassembling and expansion

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Conclusion