Probabilistic estimation framework on short term electricity load forecasting via parametric and nonparametric approaches

Download

index.pdf

Date

2014

Author

Ergin, Elçin

Metadata

Show full item record

Item Usage Stats

218
views

114
downloads

Accurate electricity load forecasting plays a crucial role for all the electricity market parties. With the deregulation of the markets, electricity load forecasting, especially for short-term, gained a great importance. Electricity load data may show different characteristics according to country, region or customer type as it has a nonlinear relationships with many of variables. Capabilities of only one model may not be enough to capture all these relationships. In this study, parametric, nonparametric and hybrid approaches are developed and employed for short-term electricity load forecasting on hourly load series from Turkish electricity market which is not studied before. These approaches include Double Seasonal Autoregressive Integrated Moving Average (DSARIMA), Nonlinear Autoregressive with Exogenous Inputs Neural Networks (NARX) and ε-Support Vector Regression (ε-SVR) and hybrid models DSARIMA-NARX and DSARIMA-ε-SVR. Additionally, we apply Local Quantile Regression (LQR) method both to combine the results obtained from the other approaches and more importantly construct probabilistic forecasts instead of providing point forecasts. Considering the multiple seasonal cycles existing in load series, examining the load data with grouping is another option to improve forecast accuracy. We also construct 4 different grouping scheme and compared their performances with the whole data results.

Subject Keywords

Energy consumption, Electric power consumption, Quantile regression., Regression analysis.

URI

http://etd.lib.metu.edu.tr/upload/12617539/index.pdf
https://hdl.handle.net/11511/23752

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

On the parametric and nonparametric prediction methods for electricity load forecasting Erişen, Esra; İyigün, Cem; Department of Industrial Engineering (2013) Accurate electricity load forecasting is of great importance in deregulated electricity markets. Market participants can reap significant financial benefits by improving their electricity load forecasts. Electricity load exhibits a complex time series structure with nonlinear relationships between the variables. Hence, new models with higher capabilities to capture such nonlinear relationships need to be developed and tested. In this thesis, we present a parametric and a nonparametric method for short-term ...
Stochastic wind-thermal generation coordination for Turkish day-ahead electricity market / Aydoğdu, Aycan; Güven, Ali Nezih; Tör, Osman Bülent; Department of Electrical and Electronics Engineering (2014) Uncertainties in wind power forecast, day-ahead and imbalance prices for the next day possess a great deal of risk to the profit of generation companies (GENCOs) participating in a day-ahead electricity market. GENCOs are exposed to imbalance penalties in the balancing market for any mismatch between their day-ahead power bids and real-time generations. Proper coordination of wind generation with thermal generation reduces this risk associated with wind uncertainty. This thesis proposes an optimal bidding a...
Mathematical modeling and solution approaches for balancing Turkish electricity day ahead market Yörükoğlu, Sinan; Avşar, Zeynep Müge; Kat, Bora; Department of Industrial Engineering (2015) In the Turkish Electricity Market, electricity trade is carried out largely through Bilateral Agreements and the emerging short term imbalances are settled in the Balancing Power Market, particularly the Day Ahead Market. In the Day Ahead Market, the participants submit their bids for each hour of the next day in the form of price-quantity pairs and the Market Operator evaluates those bids using an optimization tool. After the evaluation of the bids, a Market Clearing Price at every hour of the next day and...
The impact of photovoltaic power plant penetration level on security constrained unit commitment and an approach for reducing curtailment of PV energy Kahraman, Özgür; Ünver, Baki Zafer; Department of Electrical and Electronics Engineering (2016) As a main rule of electricity grid, the balance of generation and demand must be maintained. The system operators execute “Unit Commitment (UC)” process in day-ahead market in order to fulfill this aim. The Security Constrained Unit Commitment (SCUC) algorithm creates optimal hourly schedules for generators with minimum total electricity generation cost considering the forecasted hourly demands for the next day and the generation offers while satisfying the constraints of generators and transmission system....
Application of a Hybrid Machine Learning model on short term electricty demand prediction Assar, Ahmed Khaled Ahmed Farouk; Fahrioğlu, Murat; Sustainable Environment and Energy Systems (2022-2) Electricity demand forecasting is an important procedure in the electricity market and plays a great role in assuring a sustainable and efficient operation chain. By accurately forecasting the demand, one can see a considerable reduction in production costs as well as saving energy resources. Therefore, optimizing the demand forecasting techniques became an inseparable goal of power economics, leading to the introduction of machine learning to this sector that proved to be superior to other pre-defined alte...

Citation Formats

E. Ergin, “Probabilistic estimation framework on short term electricity load forecasting via parametric and nonparametric approaches,” M.S. - Master of Science, Middle East Technical University, 2014.