Abstract

A Smart Grid (SG) is a modernized grid to provide efficient, reliable and economic energy to the consumers. Energy is the most important resource in the world. An efficient energy distribution is required as smart devices are increasing dramatically. The forecasting of electricity consumption is supposed to be a major constituent to enhance the performance of SG. Various learning algorithms have been proposed to solve the forecasting problem. The sole purpose of this work is to predict the price and load efficiently. The first technique is Enhanced Logistic Regression (ELR) and the second technique is Enhanced Recurrent Extreme Learning Machine (ERELM). ELR is an enhanced form of Logistic Regression (LR), whereas, ERELM optimizes weights and biases using a Grey Wolf Optimizer (GWO). Classification and Regression Tree (CART), Relief-F and Recursive Feature Elimination (RFE) are used for feature selection and extraction. On the basis of selected features, classification is performed using ELR. Cross validation is done for ERELM using Monte Carlo and K-Fold methods. The simulations are performed on two different datasets. The first dataset, i.e., UMass Electric Dataset is multi-variate while the second dataset, i.e., UCI Dataset is uni-variate. The first proposed model performed better with UMass Electric Dataset than UCI Dataset and the accuracy of second model is better with UCI than UMass. The prediction accuracy is analyzed on the basis of four different performance metrics: Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Square Error (MSE) and Root Mean Square Error (RMSE). The proposed techniques are then compared with four benchmark schemes. The comparison is done to verify the adaptivity of the proposed techniques. The simulation results show that the proposed techniques outperformed benchmark schemes. The proposed techniques efficiently increased the prediction accuracy of load and price. However, the computational time is increased in both scenarios. ELR achieved almost 5% better results than Convolutional Neural Network (CNN) and almost 3% than LR. While, ERELM achieved almost 6% better results than ELM and almost 5% than RELM. However, the computational time is almost 20% increased with ELR and 50% with ERELM. Scalability is also addressed for the proposed techniques using half-yearly and yearly datasets. Simulation results show that ELR gives 5% better results while, ERELM gives 6% better results when used for yearly dataset.

Highlights

  • For electricity generation and distribution, Traditional Grids (TGs) are used

  • Simulation results show that Enhanced Logistic Regression (ELR) gives 5% better results while, Extreme Learning Machine (ERELM) gives 6% better results when used for yearly dataset

  • The first step in this model is the preprocessing of data; after the data is preprocessed, the best parameters are selected using Recurrent Extreme Learning Machine (RELM)

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Summary

Introduction

For electricity generation and distribution, Traditional Grids (TGs) are used. The infrastructure of TG is getting obsolete, which results in energy loss and less efficient output. Due to the usage of outdated infrastructure, intensive power losses are being faced. This intensive power loss leads to load shedding, which is one of the major issues of today’s world [1]. TGs use fossil fuels like coal, petrol, diesel, etc., for the combustion process of turbines. The extensive use of fossil fuels lead to natural resource depletion and increase in pollution. The literature has suggested to use Renewable

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