ELECTRICITY PRICE FORECASTING USING ADVANCED MACHINE LEARNING TECHNIQUES

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Sardana, B. (2020). ELECTRICITY PRICE FORECASTING USING ADVANCED MACHINE LEARNING TECHNIQUES. Unc Charlotte Electronic Theses And Dissertations.
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Abstract

Electricity Price Forecasts have been a very important part of the energy industry for a long time. Traditionally, it was used by utilities, but after the liberalization of electricity markets around the world, they are of prime importance not only for utilities, but also for portfolio managers, aggregators, retailers and generation companies. These different players can plan the dispatch and consumption schedule of power in accordance with price forecasts in order to maximize profit. Apart from this, electricity prices, in general, can exhibit extreme volatility. This volatility can be attributed to the change in electricity demand and the amount of energy generated/used from renewable sources (since the price of power generated from renewable sources can be very low). This calls for better modeling of electricity prices. The aim of this study is to explore various machine learning techniques like linear regression, gradient boosting, random forests, support vector machines and artificial neural networks for forecasting electricity prices of the German market. The main focus of this research is to explore the use of meta heuristic algorithm like particle swarm optimization to search for the best suited feature space for each algorithm.

Details
Author

Sardana, Bhav

Title

ELECTRICITY PRICE FORECASTING USING ADVANCED MACHINE LEARNING TECHNIQUES

Physical Description

1 online resource (78 pages) : PDF

Date

2020

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

Electricity Price Forecasts have been a very important part of the energy industry for a long time. Traditionally, it was used by utilities, but after the liberalization of electricity markets around the world, they are of prime importance not only for utilities, but also for portfolio managers, aggregators, retailers and generation companies. These different players can plan the dispatch and consumption schedule of power in accordance with price forecasts in order to maximize profit. Apart from this, electricity prices, in general, can exhibit extreme volatility. This volatility can be attributed to the change in electricity demand and the amount of energy generated/used from renewable sources (since the price of power generated from renewable sources can be very low). This calls for better modeling of electricity prices. The aim of this study is to explore various machine learning techniques like linear regression, gradient boosting, random forests, support vector machines and artificial neural networks for forecasting electricity prices of the German market. The main focus of this research is to explore the use of meta heuristic algorithm like particle swarm optimization to search for the best suited feature space for each algorithm.

Genre

masters theses

Subjects--Topics

Power resources

Degree

M.S.

Keywords

Electricity Price Forecasting
Feature Selection
Machine Learning
Swarm Optimization

Subject Area

Applied Energy & Electromechanical System

Advisor(s)

Noras, Maciej

Committee Members

Cali, Umit
Williams, Wesley
Smith, Michael

Degree Note

Thesis (M.S.)--University of North Carolina at Charlotte, 2020.

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Rights Holder Information

Copyright is held by the author unless otherwise indicated.

Identifier

Sardana_uncc_0694N_12443

Permalink

http://hdl.handle.net/20.500.13093/etd:2512