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Higher renewable energy integration into the existing energy system of Finland – Is there any maximum limit?
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“Such systems use machine learning algorithms to forecast when the sun or wind will be available, and energy distribution is then appropriately re-allocated to ensure a constant supply and diminish the use of non-renewable energy sources43–45.”
From A decision-support framework for evaluating AI-enabled ESG strategies in the context of sustainable manufacturing systems (2025) · cites this paper as [45]
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Metadata
- Authors
- Zakeri, B., Syri, S., & Rinne, S.
- Affiliations
- Aalto University (FI)
- Corresponding author
- Zakeri, Behnam
- Venue
- Energy
- Publisher
- Elsevier BV
- Published
- 2015-12
- Volume
- 92
- Pages
- 244-259
- ISSN
- 0360-5442 (ISSN-L)
- OpenAlex
- W2019412828
- Cited by
- 107
- References
- 102
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 7 · 2023: 8 · 2022: 6 · 2021: 11 · 2020: 14 · 2019: 12 · 2018: 17 · 2017: 13 · 2016: 9 · 2015: 4
- FWCI
- 6.91
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Energy (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Aalto University's Energy Efficiency research program
- Aalto ENG Doctoral School
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Integrated Energy Systems Optimization
- Physical Sciences › Energy › Energy Engineering and Power Technology › Hybrid Renewable Energy Systems
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Renewable Energy and Sustainability
- Keywords
- renewable energy integration, energy flexibility, wind energy, solar energy, bioenergy, hydropower, solar heating and cooling, photovoltaics, heat pumps, energy mix optimization, carbon emission reduction, energy efficiency, energy storage
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