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Classification and Regression Based Methods for Short Term Load and Price Forecasting: A Survey

Conference paper
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Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 47)

Abstract

Due to increase in electronic appliances, electricity is becoming basic necessity of life. Consumption of electricity depends on various factors like temperature, wind, humidity, weekend, working days and season. In electricity load forecasting, many researchers perform data analysis on electricity data provided by utilities to extract meaningful information. Smart Grid (SG) is power supply network which allows consumers to monitor their energy usage. It integrates different components of electricity like variety of operations, smart appliances, data collected from smart meters and efficient energy sources. To reduce the consumption of electricity, accurate prediction is compulsory. A good forecasting model makes an acceptable use of all characteristics of electric loads based data and also reduces dimensionality of that data. Various machine learning techniques are proposed for load forecasting in literature. In this research, we present a survey based on different short term electricity forecasting techniques for price and load. We broadly categorized different types of techniques into traditional machine learning and deep learning techniques.

Keywords

Load forecasting Support Vector Machine Smart Grid Price forecasting Deep learning Neural network 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.COMSATS University IslamabadIslamabadPakistan

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