The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Genetic-Algorithm-Based Optimization Approach for Energy Management
Abstract
This paper proposes a new strategy to meet the controllable heating, ventilation, and air conditioning (HVAC) load with a hybrid-renewable generation and energy storage system. Historical hourly wind speed, solar irradiance, and load data are used to stochastically model the wind generation, photovoltaic generation, and load. Using fuzzy C-Means (FCM) clustering, these data are grouped into 10 clusters of days with similar data points to account for seasonal variations. In order to minimize cost and increase efficiency, we use a GA-based optimization approach together with a two-point estimate method. Minimizing the cost function guarantees minimum PV and wind generation installation as well as storage capacity selection to supply the HVAC load. Different scenarios are examined to evaluate the efficiency of the system with different percentages of load shifting. The maximum capacity of the storage system and excess energy are calculated as the most important indices for energy efficiency assessment. The cumulative distribution functions of these indices are plotted and compared. A smart-grid strategy is developed for matching renewable energy generation (solar and wind) with the HVAC load.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Pattern Recognition with Fuzzy Objective Function Algorithms
Bezdek, J. C. (1981) · doi:10.1007/978-1-4757-0450-1 · Cited by 9,881
(2005) · Wiley eBooks · doi:10.1002/0470012684 · Cited by 2,915
THE APPLICATION OF CLUSTER ANALYSIS IN STRATEGIC MANAGEMENT RESEARCH: AN ANALYSIS AND CRITIQUE
KETCHEN Jr., D. J., & SHOOK, C. L. (1996) · Strategic Management Journal · doi:10.1002/(sici)1097-0266(199606)17:6<441::aid-smj819>3.0.co;2-g · Cited by 2,375
Statistical Models in Engineering
Brownlee, K. A. (1968) · Technometrics · doi:10.1080/00401706.1968.10490554 · Cited by 1,201
Borowy, B. S., & Salameh, Z. M. (1996) · IEEE Transactions on Energy Conversion · doi:10.1109/60.507648 · Cited by 944
Point estimates for probability moments
Rosenblueth, E. (1975) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.72.10.3812 · Cited by 868
Power Management of a Stand-Alone Wind/Photovoltaic/Fuel Cell Energy System
Wang, C., & Nehrir, M. H. (2008) · IEEE Transactions on Energy Conversion · doi:10.1109/tec.2007.914200 · Cited by 787
Multi-objective design of PV–wind–diesel–hydrogen–battery systems
Dufo-López, R., & Bernal-Agustín, J. L. (2008) · Renewable Energy · doi:10.1016/j.renene.2008.02.027 · Cited by 548
Hybrid solar/wind power system probabilistic modelling for long-term performance assessment
Tina, G., Gagliano, S., & Raiti, S. (2006) · Solar Energy · doi:10.1016/j.solener.2005.03.013 · Cited by 413
Distribution System Planning With Incorporating DG Reactive Capability and System Uncertainties
Zou, K., Agalgaonkar, A. P., Muttaqi, K. M., & Perera, S. (2012) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2011.2166281 · Cited by 367
Cited by
Solar–wind hybrid renewable energy system: A review
Khare, V., Nema, S., & Baredar, P. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.12.223 · Cited by 836
Serale, G., Fiorentini, M., Capozzoli, A., Bernardini, D., & Bemporad, A. (2018) · Energies · doi:10.3390/en11030631 · Cited by 637
Prosumer based energy management and sharing in smart grid
Zafar, R., Mahmood, A., Razzaq, S., Ali, W., Naeem, U., & Shehzad, K. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.07.018 · Cited by 458
Decision making in renewable energy investments: a review
Strantzali, E., & Aravossis, K. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.11.021 · Cited by 400
Anoune, K., Bouya, M., Astito, A., & Abdellah, A. B. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2018.05.032 · Cited by 382
Metadata
- Authors
- Arabali, A., Ghofrani, M., Etezadi-Amoli, M., Fadali, M. S., & Baghzouz, Y.
- Venue
- IEEE Transactions on Power Delivery
- Published
- 2013-01
- Volume
- 28
- Issue
- 1
- Pages
- 162-170
- ISSN
- 0885-8977 (ISSN-L)
- 1937-4208
- OpenAlex
- W1969192618
- Cited by
- 388
- References
- 20
- FWCI
- 11.80
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Genetic-Algorithm-Based Optimization Approach for Energy Management. https://app.cochat.ai/papers/10.1109/tpwrd.2012.2219598
- The paper
- Arabali, A., Ghofrani, M., Etezadi-Amoli, M., Fadali, M. S., & Baghzouz, Y. (2013). Genetic-Algorithm-Based Optimization Approach for Energy Management. IEEE Transactions on Power Delivery. https://doi.org/10.1109/tpwrd.2012.2219598
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
