Abstract

Short-Term Electricity Load Forecasting (STELF) through Data Analytics (DA) is an emerging and active research area. Forecasting about electricity load and price provides future trends and patterns of consumption. There is a loss in generation and use of electricity. So, multiple strategies are used to solve the aforementioned problems. Day-ahead electricity price and load forecasting are beneficial for both suppliers and consumers. In this paper, Deep Learning (DL) and data mining techniques are used for electricity load and price forecasting. XG-Boost (XGB), Decision Tree (DT), Recursive Feature Elimination (RFE) and Random Forest (RF) are used for feature selection and feature extraction. Enhanced Convolutional Neural Network (ECNN) and Enhanced Support Vector Regression (ESVR) are used as classifiers. Grid Search (GS) is used for tuning of the parameters of classifiers to increase their performance. The risk of over-fitting is mitigated by adding multiple layers in ECNN. Finally, the proposed models are compared with different benchmark schemes for stability analysis. The performance metrics MSE, RMSE, MAE, and MAPE are used to evaluate the performance of the proposed models. The experimental results show that the proposed models outperformed other benchmark schemes. ECNN performed well with threshold 0.08 for load forecasting. While ESVR performed better with threshold value 0.15 for price forecasting. ECNN achieved almost 2% better accuracy than CNN. Furthermore, ESVR achieved almost 1% better accuracy than the existing scheme (SVR).

Highlights

  • Nowadays, electricity plays an important role in economic and social development

  • Dataset 1 is used for a load prediction model, which is taken from Independent System Operator New England (ISO-NE) and it contains hourly data of a load of electricity

  • Where dataset 2 is used as input in the price prediction model, which is taken from NYISO

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Summary

Introduction

Electricity plays an important role in economic and social development. Our lives are imagined to be stuck. Electricity usage areas are divided into three categories: industrial, commercial and residential. According to [1], residential area consumes almost 65% of the electricity of whole generation. Most of the electricity is wasted during generation, transmission and distribution. Smart Grid (SG) is introduced to solve the aforementioned issues. A Traditional Grid is converted into SG by integrating Information and Communications Technology (ICT) with it. SG is an intelligent grid system that manages the Electronics 2019, 8, 122; doi:10.3390/electronics8020122 www.mdpi.com/journal/electronics

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