Hybrid Forecasting Model for Short-Term Electricity Market Prices with Renewable Integration

Gerardo J. Osório, Mohamed Lotfi, Miadreza Shafie-khah, Vasco M. A. Campos and João P. S. Catalão
Additional contact information
Gerardo J. Osório: Center for Mechanical and Aerospace Science and Technologies, University of Beira Interior, 6201-001 Covilhã, Portugal
Mohamed Lotfi: INESC TEC, 4200-465 Porto, Portugal
Miadreza Shafie-khah: INESC TEC, 4200-465 Porto, Portugal
Vasco M. A. Campos: Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal
João P. S. Catalão: INESC TEC, 4200-465 Porto, Portugal

Sustainability, 2018, vol. 11, issue 1, 1-15

Abstract: In recent years, there have been notable commitments and obligations by the electricity sector for more sustainable generation and delivery processes to reduce the environmental footprint. However, there is still a long way to go to achieve necessary sustainability goals while ensuring standards of robustness and the quality of power grids. One of the main challenges hindering this progress are uncertainties and stochasticity associated with the electricity sector and especially renewable generation. In this paradigm shift, forecasting tools are indispensable, and their utilization can significantly improve system operation and minimize costs associated with all related activities. Thus, forecasting tools have an essential key role in all decision-making stages. In this work, a hybrid probabilistic forecasting model (HPFM) was developed for short-term electricity market prices (EMP) combining wavelet transforms (WT), hybrid particle swarm optimization (DEEPSO), adaptive neuro-fuzzy inference system (ANFIS), and Monte Carlo simulation (MCS). The proposed hybrid probabilistic forecasting model (HPFM) was tested and validated with real data from the Spanish and Pennsylvania-New Jersey-Maryland (PJM) markets. The proposed model exhibited favorable results and performance in comparison with previously published work considering electricity market prices (EMP) data, which is notable.

Keywords: adaptive neuro-fuzzy inference system; electricity market prices; forecasting; particle swarm optimization; probabilistic; Monte Carlo simulation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.mdpi.com/2071-1050/11/1/57/pdf (application/pdf)
https://www.mdpi.com/2071-1050/11/1/57/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:11:y:2018:i:1:p:57-:d:192550

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().