Short Term Electric Power Load Forecasting Using Principal Component Analysis and Recurrent Neural Networks

Venkataramana Veeramsetty,Marco Mussetta,Dongari Rakesh Chandra,Francesco Grimaccia

doi:10.3390/forecast4010008

Venkataramana Veeramsetty, Marco Mussetta + Show 2 more

Open Access

https://doi.org/10.3390/forecast4010008

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Abstract

Electrical load forecasting study is required in electric power systems for different applications with respect to the specific time horizon, such as optimal operations, grid stability, Demand Side Management (DSM) and long-term strategic planning. In this context, machine learning and data analytics models represent a valuable tool to cope with the intrinsic complexity and especially design future demand-side advanced services. The main novelty in this paper is that the combination of a Recurrent Neural Network (RNN) and Principal Component Analysis (PCA) techniques is proposed to improve the forecasting capability of the hourly load on an electric power substation. A historical dataset of measured loads related to a 33/11 kV MV substation is considered in India as a case study, in order to properly validate the designed method. Based on the presented numerical results, the proposed approach proved itself to accurately predict loads with a reduced dimensionality of input data, thus minimizing the overall computational effort.

Highlights

Nowadays, the energy system is facing a radical revolution towards a green transition, with increasing penetration of renewable energy sources (RES), migration to distributed systems, with new actors like prosumers, and storage integration, both utility scale and domestic, which represent a key technology to decouple energy production and consumption [1].In this regard, distributed sensor architectures, digital technology, data analytics and computational tools would represent crucial enabling technologies for monitoring, forecasting and maintenance purposes, to better manage the balance between power demand and supply, and to improve embedding of distributed RES; for the particular case of stand-alone hybrid systems, energy forecasting will help anticipating customers’ behavior, sizing the electrical infrastructure and improving overall system reliability [2]
Electrical load forecasting study is required in electric power systems for different applications with respect to the specific time horizon, such as optimal operations, grid stability, Demand Side Management (DSM) and long-term strategic planning
In order to improve the forecasting accuracy and to build a light weight model for active power load forecasting applied to a 33/11 kV substation, a new approach was developed in this paper by using recurrent neural networks for load forecasting and Principal Component Analysis for dimensional reduction

Summary

Introduction

The energy system is facing a radical revolution towards a green transition, with increasing penetration of renewable energy sources (RES), migration to distributed systems, with new actors like prosumers, and storage integration, both utility scale and domestic, which represent a key technology to decouple energy production and consumption [1]. Most of the papers on probabilistic renewable generation forecasting literature over the last ten years or so have focused on different variants of statistical and machine learning approaches: in [23] a comparison of non-parametric approaches to this probabilistic forecasting problem has been performed All these methodologies in literature contributed significantly to face short-term electric power load forecasting problems. In order to improve the forecasting accuracy and to build a light weight model for active power load forecasting applied to a 33/11 kV substation, a new approach was developed in this paper by using recurrent neural networks for load forecasting and Principal Component Analysis for dimensional reduction. All these methodologies provides valuable contribution towards short-term load forecasting but have some limitations like model complexity, accuracy and weekly impact not considered.

Result

Comparative Result Analysis

Findings

Conclusions