Energy Performance Analysis of Photovoltaic Integrated with Microgrid Data Analysis Using Deep Learning Feature Selection and Classification Techniques Article Swipe
Sana Qaiyum
,
Martin Margala
,
Pravin R. Kshirsagar
,
Prąsun Chakrabarti
,
Kashif Irshad
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/su151411081
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/su151411081
Microgrids are an essential element of smart grids, which contain distributed renewable energy sources (RESs), energy storage devices, and load control strategies. Models built based on machine learning (ML) and deep learning (DL) offer hope for anticipating consumer demands and energy production from RESs. This study suggests an innovative approach for energy analysis based on the feature extraction and classification of microgrid photovoltaic cell data using deep learning algorithms. The energy optimization of a microgrid was carried out using a photovoltaic energy system with distributed power generation. The data analysis has been carried out for feature analysis and classification using a Gaussian radial Boltzmann with Markov encoder model. Based on microgrid energy optimization and data analysis, an experimental analysis of power analysis, energy efficiency, quality of service (QoS), accuracy, precision, and recall has been conducted. The proposed technique attained power analysis of 88%, energy efficiency of 95%, QoS of 77%, accuracy of 93%, precision of 85%, and recall of 77%.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/su151411081
- https://www.mdpi.com/2071-1050/15/14/11081/pdf?version=1689414494
- OA Status
- gold
- Cited By
- 11
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4384557713
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4384557713Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/su151411081Digital Object Identifier
- Title
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Energy Performance Analysis of Photovoltaic Integrated with Microgrid Data Analysis Using Deep Learning Feature Selection and Classification TechniquesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-07-15Full publication date if available
- Authors
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Sana Qaiyum, Martin Margala, Pravin R. Kshirsagar, Prąsun Chakrabarti, Kashif IrshadList of authors in order
- Landing page
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https://doi.org/10.3390/su151411081Publisher landing page
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https://www.mdpi.com/2071-1050/15/14/11081/pdf?version=1689414494Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2071-1050/15/14/11081/pdf?version=1689414494Direct OA link when available
- Concepts
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Microgrid, Photovoltaic system, Computer science, Feature selection, Renewable energy, Feature extraction, Artificial intelligence, Machine learning, Engineering, Data mining, Electrical engineering, Control (management)Top concepts (fields/topics) attached by OpenAlex
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11Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 8, 2023: 1Per-year citation counts (last 5 years)
- References (count)
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22Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W4211093334, https://openalex.org/W3149578452, https://openalex.org/W4361030035, https://openalex.org/W3198049210, https://openalex.org/W4303685474, https://openalex.org/W2990297540, https://openalex.org/W2960560113, https://openalex.org/W3199109943, https://openalex.org/W4285600350, https://openalex.org/W4290996193, https://openalex.org/W4206718650, https://openalex.org/W3152210602, https://openalex.org/W3202758697, https://openalex.org/W3165230167, https://openalex.org/W3179316720, https://openalex.org/W4283159452, https://openalex.org/W3181669971, https://openalex.org/W3210451168, https://openalex.org/W3085898885, https://openalex.org/W4226086227, https://openalex.org/W4294631267, https://openalex.org/W3124819130 |
| referenced_works_count | 22 |
| abstract_inverted_index.a | 73, 79, 100 |
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| abstract_inverted_index.on | 25, 54, 109 |
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| abstract_inverted_index.and | 18, 29, 39, 58, 97, 113, 130, 156 |
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| abstract_inverted_index.was | 75 |
| abstract_inverted_index.(DL) | 32 |
| abstract_inverted_index.(ML) | 28 |
| abstract_inverted_index.77%, | 149 |
| abstract_inverted_index.77%. | 159 |
| abstract_inverted_index.85%, | 155 |
| abstract_inverted_index.88%, | 142 |
| abstract_inverted_index.93%, | 152 |
| abstract_inverted_index.95%, | 146 |
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| abstract_inverted_index.which | 8 |
| abstract_inverted_index.(QoS), | 127 |
| abstract_inverted_index.Markov | 105 |
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| abstract_inverted_index.energy | 12, 15, 40, 51, 70, 81, 111, 122, 143 |
| abstract_inverted_index.grids, | 7 |
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| abstract_inverted_index.radial | 102 |
| abstract_inverted_index.recall | 131, 157 |
| abstract_inverted_index.system | 82 |
| abstract_inverted_index.(RESs), | 14 |
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| abstract_inverted_index.feature | 56, 95 |
| abstract_inverted_index.machine | 26 |
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| abstract_inverted_index.service | 126 |
| abstract_inverted_index.sources | 13 |
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| abstract_inverted_index.accuracy | 150 |
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| abstract_inverted_index.devices, | 17 |
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| abstract_inverted_index.optimization | 71, 112 |
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| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5078391471 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I79516672 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.8799999952316284 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.88873738 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |