Next Article in Journal
Optimal Dispatch of a Virtual Power Plant Considering Demand Response and Carbon Trading
Next Article in Special Issue
Forecasting the Long-Term Wind Data via Measure-Correlate-Predict (MCP) Methods
Previous Article in Journal
Computational Fluid Dynamics (CFD) Investigation of Wind Turbine Nacelle Separation Accident over Complex Terrain in Japan
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Energies
Volume 11
Issue 6
10.3390/en11061487
energies-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Computational Intelligence Techniques Applied to the Day Ahead PV Output Power Forecast: PHANN, SNO and Mixed

by
Emanuele Ogliari
*,
Alessandro Niccolai
*,
Sonia Leva
and
Riccardo E. Zich
Dipartimento di Energia, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy
*
Authors to whom correspondence should be addressed.
Energies 2018, 11(6), 1487; https://doi.org/10.3390/en11061487
Submission received: 29 April 2018 / Revised: 1 June 2018 / Accepted: 4 June 2018 / Published: 7 June 2018
(This article belongs to the Special Issue Solar and Wind Energy Forecasting)
Browse Figures
Versions Notes

Abstract

An accurate forecast of the exploitable energy from Renewable Energy Sources is extremely important for the stability issues of the electric grid and the reliability of the bidding markets. This paper presents a comparison among different forecasting methods of the photovoltaic output power introducing a new method that mixes some peculiarities of the others: the Physical Hybrid Artificial Neural Network and the five parameters model estimated by the Social Network Optimization. In particular, the day-ahead forecasts evaluated against real data measured for two years in an existing photovoltaic plant located in Milan, Italy, are compared by means both new and the most common error indicators. Results reported in this work show the best forecasting capability of the new “mixed method” which scored the best forecast skill and Enveloped Mean Absolute Error on a yearly basis (47% and 24.67%, respectively).
Keywords: solar power; computational intelligence; day-ahead forecast; Artificial Neural Network; five parameters model; Social Network Optimization solar power; computational intelligence; day-ahead forecast; Artificial Neural Network; five parameters model; Social Network Optimization

Share and Cite

MDPI and ACS Style

Ogliari, E.; Niccolai, A.; Leva, S.; Zich, R.E. Computational Intelligence Techniques Applied to the Day Ahead PV Output Power Forecast: PHANN, SNO and Mixed. Energies 2018, 11, 1487. https://doi.org/10.3390/en11061487

AMA Style

Ogliari E, Niccolai A, Leva S, Zich RE. Computational Intelligence Techniques Applied to the Day Ahead PV Output Power Forecast: PHANN, SNO and Mixed. Energies. 2018; 11(6):1487. https://doi.org/10.3390/en11061487

Chicago/Turabian Style

Ogliari, Emanuele, Alessandro Niccolai, Sonia Leva, and Riccardo E. Zich. 2018. "Computational Intelligence Techniques Applied to the Day Ahead PV Output Power Forecast: PHANN, SNO and Mixed" Energies 11, no. 6: 1487. https://doi.org/10.3390/en11061487

APA Style

Ogliari, E., Niccolai, A., Leva, S., & Zich, R. E. (2018). Computational Intelligence Techniques Applied to the Day Ahead PV Output Power Forecast: PHANN, SNO and Mixed. Energies, 11(6), 1487. https://doi.org/10.3390/en11061487

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop