Clustering Informed MLP Models for Fast and Accurate Short-Term Load Forecasting

Athanasios Ioannis Arvanitidis,Dimitrios Kontogiannis,Ioannis P Panapakidis,Dimitrios Bargiotas,Aspassia Daskalopulu,Lefteri H Tsoukalas

doi:10.3390/en15041295

Athanasios Ioannis Arvanitidis, Dimitrios Kontogiannis + Show 4 more

Open Access

https://doi.org/10.3390/en15041295

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Journal: Energies	Publication Date: Feb 10, 2022
Citations: 10	License type: CC BY 4.0

Affiliation: University of Thessaly, Purdue University West Lafayette

Abstract

The stable and efficient operation of power systems requires them to be optimized, which, given the growing availability of load data, relies on load forecasting methods. Fast and highly accurate Short-Term Load Forecasting (STLF) is critical for the daily operation of power plants, and state-of-the-art approaches for it involve hybrid models that deploy regressive deep learning algorithms, such as neural networks, in conjunction with clustering techniques for the pre-processing of load data before they are fed to the neural network. This paper develops and evaluates four robust STLF models based on Multi-Layer Perceptrons (MLPs) coupled with the K-Means and Fuzzy C-Means clustering algorithms. The first set of two models cluster the data before feeding it to the MLPs, and are directly comparable to similar existing approaches, yielding, however, better forecasting accuracy. They also serve as a common reference point for the evaluation of the second set of two models, which further enhance the input to the MLP by informing it explicitly with clustering information, which is a novel feature. All four models are designed, tested and evaluated using data from the Greek power system, although their development is generic and they could, in principle, be applied to any power system. The results obtained by the four models are compared to those of other STLF methods, using objective metrics, and the accuracy obtained, as well as convergence time, is in most cases improved.

Highlights

We present the results obtained from the four Short-Term Load Forecasting (STLF) models that emerged from the two experiments
This paper examines the integration of clustering algorithms with neural networks for the purposes of developing fast and accurate STLF models
The first set of models followed the standard for hybrid STLF model development, in which first the dataset is clustered and each cluster is used to train a Multi-Layer Perceptrons (MLPs)

Summary

Introduction

Electric load forecasting has, justifiably, been the focus of much research, and work in the area is classified into three categories based on the time horizon and the operational choice that must be made, namely short-term, medium-term, and long-term forecasting. Long-term load forecasting generally spans 20 years and is required for planning purposes, such as the construction of new power plants and the upgrade of transmission system capacity. Medium term load forecasting ranges from a few weeks to a year and is mostly used for scheduling maintenance and fuel supply [7]. The day-to-day functioning of the power system necessitates Short-Term Load Forecasting (STLF), which is primarily influenced by temporal factors (for example, weekly periodicity and seasonal fluctuations) and weather conditions (for example, humidity, temperature, wind speed, and cloud coverage) [8]. In order to achieve high accuracy in forecasting results, various load forecasting models have been developed and investigated [9]

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