Solar energy forecasting and optimization system for efficient renewable energy integration

Diana Manjarres; Ricardo Alonso; Sergio Gil-Lopez; Itziar Landa-Torres

doi:10.1007/978-3-319-71643-5_1

Solar energy forecasting and optimization system for efficient renewable energy integration

Diana Manjarres, Ricardo Alonso, Sergio Gil-Lopez, Itziar Landa-Torres

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

Solar energy forecasting represents a key issue in order to efficiently manage the supply-demand balance and promote an effective renewable energy integration. In this regard, an accurate solar energy forecast is of utmoss importance for avoiding large voltage variations into the electricity network and providing the system with mechanisms for managing the produced energy in an optimal way. This paper presents a novel solar energy forecasting and optimization approach called SUNSET which efficiently determines the optimal energy management for the next 24 h in terms of: self-consumption, energy purchase and battery energy storage for later consumption. The proposed SUNSET approach has been tested in a real solar PV system plant installed in Zamudio (Spain) and compared towards a Real-Time (RT) strategy in terms of price and energy savings obtaining attractive results.

Original language	English
Title of host publication	Data Analytics for Renewable Energy Integration
Subtitle of host publication	Informing the Generation and Distribution of Renewable Energy - 5th ECML PKDD Workshop, DARE 2017, Revised Selected Papers
Editors	Oliver Kramer, Stuart Madnick, Wei Lee Woon, Zeyar Aung
Publisher	Springer Verlag
Pages	1-12
Number of pages	12
ISBN (Print)	9783319716428
DOIs	https://doi.org/10.1007/978-3-319-71643-5_1
Publication status	Published - 2017
Event	5th International Workshop on Data Analytics for Renewable Energy Integration, DARE 2017 - Skopje, Macedonia, The Former Yugoslav Republic of Duration: 22 Sept 2017 → 22 Sept 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10691 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th International Workshop on Data Analytics for Renewable Energy Integration, DARE 2017
Country/Territory	Macedonia, The Former Yugoslav Republic of
City	Skopje
Period	22/09/17 → 22/09/17

Keywords

Optimization
PV energy forecast
Renewable energy integration
Solar energy

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-319-71643-5_1

Cite this

Manjarres, D., Alonso, R., Gil-Lopez, S., & Landa-Torres, I. (2017). Solar energy forecasting and optimization system for efficient renewable energy integration. In O. Kramer, S. Madnick, W. L. Woon, & Z. Aung (Eds.), Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy - 5th ECML PKDD Workshop, DARE 2017, Revised Selected Papers (pp. 1-12). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10691 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-71643-5_1

Manjarres, Diana ; Alonso, Ricardo ; Gil-Lopez, Sergio et al. / Solar energy forecasting and optimization system for efficient renewable energy integration. Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy - 5th ECML PKDD Workshop, DARE 2017, Revised Selected Papers. editor / Oliver Kramer ; Stuart Madnick ; Wei Lee Woon ; Zeyar Aung. Springer Verlag, 2017. pp. 1-12 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{99f08fe793604512a1b28dd45c46cd65,

title = "Solar energy forecasting and optimization system for efficient renewable energy integration",

abstract = "Solar energy forecasting represents a key issue in order to efficiently manage the supply-demand balance and promote an effective renewable energy integration. In this regard, an accurate solar energy forecast is of utmoss importance for avoiding large voltage variations into the electricity network and providing the system with mechanisms for managing the produced energy in an optimal way. This paper presents a novel solar energy forecasting and optimization approach called SUNSET which efficiently determines the optimal energy management for the next 24 h in terms of: self-consumption, energy purchase and battery energy storage for later consumption. The proposed SUNSET approach has been tested in a real solar PV system plant installed in Zamudio (Spain) and compared towards a Real-Time (RT) strategy in terms of price and energy savings obtaining attractive results.",

keywords = "Optimization, PV energy forecast, Renewable energy integration, Solar energy",

author = "Diana Manjarres and Ricardo Alonso and Sergio Gil-Lopez and Itziar Landa-Torres",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 5th International Workshop on Data Analytics for Renewable Energy Integration, DARE 2017 ; Conference date: 22-09-2017 Through 22-09-2017",

year = "2017",

doi = "10.1007/978-3-319-71643-5_1",

language = "English",

isbn = "9783319716428",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "1--12",

editor = "Oliver Kramer and Stuart Madnick and Woon, {Wei Lee} and Zeyar Aung",

booktitle = "Data Analytics for Renewable Energy Integration",

address = "Germany",

}

Manjarres, D , Alonso, R , Gil-Lopez, S & Landa-Torres, I 2017, Solar energy forecasting and optimization system for efficient renewable energy integration. in O Kramer, S Madnick, WL Woon & Z Aung (eds), Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy - 5th ECML PKDD Workshop, DARE 2017, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10691 LNAI, Springer Verlag, pp. 1-12, 5th International Workshop on Data Analytics for Renewable Energy Integration, DARE 2017, Skopje, Macedonia, The Former Yugoslav Republic of, 22/09/17. https://doi.org/10.1007/978-3-319-71643-5_1

Solar energy forecasting and optimization system for efficient renewable energy integration. / Manjarres, Diana ; Alonso, Ricardo ; Gil-Lopez, Sergio et al.
Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy - 5th ECML PKDD Workshop, DARE 2017, Revised Selected Papers. ed. / Oliver Kramer; Stuart Madnick; Wei Lee Woon; Zeyar Aung. Springer Verlag, 2017. p. 1-12 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10691 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Solar energy forecasting and optimization system for efficient renewable energy integration

AU - Manjarres, Diana

AU - Alonso, Ricardo

AU - Gil-Lopez, Sergio

AU - Landa-Torres, Itziar

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - Solar energy forecasting represents a key issue in order to efficiently manage the supply-demand balance and promote an effective renewable energy integration. In this regard, an accurate solar energy forecast is of utmoss importance for avoiding large voltage variations into the electricity network and providing the system with mechanisms for managing the produced energy in an optimal way. This paper presents a novel solar energy forecasting and optimization approach called SUNSET which efficiently determines the optimal energy management for the next 24 h in terms of: self-consumption, energy purchase and battery energy storage for later consumption. The proposed SUNSET approach has been tested in a real solar PV system plant installed in Zamudio (Spain) and compared towards a Real-Time (RT) strategy in terms of price and energy savings obtaining attractive results.

AB - Solar energy forecasting represents a key issue in order to efficiently manage the supply-demand balance and promote an effective renewable energy integration. In this regard, an accurate solar energy forecast is of utmoss importance for avoiding large voltage variations into the electricity network and providing the system with mechanisms for managing the produced energy in an optimal way. This paper presents a novel solar energy forecasting and optimization approach called SUNSET which efficiently determines the optimal energy management for the next 24 h in terms of: self-consumption, energy purchase and battery energy storage for later consumption. The proposed SUNSET approach has been tested in a real solar PV system plant installed in Zamudio (Spain) and compared towards a Real-Time (RT) strategy in terms of price and energy savings obtaining attractive results.

KW - Optimization

KW - PV energy forecast

KW - Renewable energy integration

KW - Solar energy

UR - http://www.scopus.com/inward/record.url?scp=85036664509&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-71643-5_1

DO - 10.1007/978-3-319-71643-5_1

M3 - Conference contribution

AN - SCOPUS:85036664509

SN - 9783319716428

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 1

EP - 12

BT - Data Analytics for Renewable Energy Integration

A2 - Kramer, Oliver

A2 - Madnick, Stuart

A2 - Woon, Wei Lee

A2 - Aung, Zeyar

PB - Springer Verlag

T2 - 5th International Workshop on Data Analytics for Renewable Energy Integration, DARE 2017

Y2 - 22 September 2017 through 22 September 2017

ER -

Manjarres D , Alonso R , Gil-Lopez S, Landa-Torres I. Solar energy forecasting and optimization system for efficient renewable energy integration. In Kramer O, Madnick S, Woon WL, Aung Z, editors, Data Analytics for Renewable Energy Integration: Informing the Generation and Distribution of Renewable Energy - 5th ECML PKDD Workshop, DARE 2017, Revised Selected Papers. Springer Verlag. 2017. p. 1-12. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-71643-5_1

Solar energy forecasting and optimization system for efficient renewable energy integration

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this