Scheduling Appliances with GA, TLBO, FA, OSR and Their Hybrids Using Chance Constrained Optimization for Smart Homes Article Swipe
Zunaira Nadeem
,
Nadeem Javaid
,
Asad Waqar Malik
,
Sohail Iqbal
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.3390/en11040888
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.3390/en11040888
In this paper, we design a controller for home energy management based on following meta-heuristic algorithms: teaching learning-based optimization (TLBO), genetic algorithm (GA), firefly algorithm (FA) and optimal stopping rule (OSR) theory. The principal goal of designing this controller is to reduce the energy consumption of residential sectors while reducing consumer’s electricity bill and maximizing user comfort. Additionally, we propose three hybrid schemes OSR-GA, OSR-TLBO and OSR-FA, by combining the best features of existing algorithms. We have also optimized the desired parameters: peak to average ratio, energy consumption, cost, and user comfort (appliance waiting time) for 20, 50, 100 and 200 heterogeneous homes in two steps. In the first step, we obtain the optimal scheduling of home appliances implementing our aforementioned hybrid schemes for single and multiple homes while considering user preferences and threshold base policy. In the second step, we formulate our problem through chance constrained optimization. Simulation results show that proposed hybrid scheduling schemes outperformed for single and multiple homes and they shift the consumer load demand exceeding a predefined threshold to the hours where the electricity price is low thus following the threshold base policy. This helps to reduce electricity cost while considering the comfort of a user by minimizing delay and peak to average ratio. In addition, chance-constrained optimization is used to ensure the scheduling of appliances while considering the uncertainties of a load hence smoothing the load curtailment. The major focus is to keep the appliances power consumption within the power constraint, while keeping power consumption below a pre-defined acceptable level. Moreover, the feasible regions of appliances electricity consumption are calculated which show the relationship between cost and energy consumption and cost and waiting time.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/en11040888
- https://www.mdpi.com/1996-1073/11/4/888/pdf?version=1525347960
- OA Status
- gold
- Cited By
- 56
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2797476311
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2797476311Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/en11040888Digital Object Identifier
- Title
-
Scheduling Appliances with GA, TLBO, FA, OSR and Their Hybrids Using Chance Constrained Optimization for Smart HomesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-04-10Full publication date if available
- Authors
-
Zunaira Nadeem, Nadeem Javaid, Asad Waqar Malik, Sohail IqbalList of authors in order
- Landing page
-
https://doi.org/10.3390/en11040888Publisher landing page
- PDF URL
-
https://www.mdpi.com/1996-1073/11/4/888/pdf?version=1525347960Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1996-1073/11/4/888/pdf?version=1525347960Direct OA link when available
- Concepts
-
Demand response, Computer science, Mathematical optimization, Scheduling (production processes), Electricity, Energy consumption, Firefly algorithm, Genetic algorithm, Smart grid, Real-time computing, Automotive engineering, Engineering, Particle swarm optimization, Algorithm, Mathematics, Electrical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
56Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 5, 2023: 8, 2022: 5, 2021: 13Per-year citation counts (last 5 years)
- References (count)
-
40Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W2797476311 |
|---|---|
| doi | https://doi.org/10.3390/en11040888 |
| ids.doi | https://doi.org/10.3390/en11040888 |
| ids.mag | 2797476311 |
| ids.openalex | https://openalex.org/W2797476311 |
| fwci | 3.92807586 |
| type | article |
| title | Scheduling Appliances with GA, TLBO, FA, OSR and Their Hybrids Using Chance Constrained Optimization for Smart Homes |
| biblio.issue | 4 |
| biblio.volume | 11 |
| biblio.last_page | 888 |
| biblio.first_page | 888 |
| topics[0].id | https://openalex.org/T10603 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 1.0 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2208 |
| topics[0].subfield.display_name | Electrical and Electronic Engineering |
| topics[0].display_name | Smart Grid Energy Management |
| topics[1].id | https://openalex.org/T10121 |
| topics[1].field.id | https://openalex.org/fields/22 |
| topics[1].field.display_name | Engineering |
| topics[1].score | 0.9948999881744385 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2215 |
| topics[1].subfield.display_name | Building and Construction |
| topics[1].display_name | Building Energy and Comfort Optimization |
| topics[2].id | https://openalex.org/T11954 |
| topics[2].field.id | https://openalex.org/fields/21 |
| topics[2].field.display_name | Energy |
| topics[2].score | 0.9890000224113464 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2105 |
| topics[2].subfield.display_name | Renewable Energy, Sustainability and the Environment |
| topics[2].display_name | Energy Efficiency and Management |
| is_xpac | False |
| apc_list.value | 2200 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2382 |
| apc_paid.value | 2200 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2382 |
| concepts[0].id | https://openalex.org/C2779438525 |
| concepts[0].level | 3 |
| concepts[0].score | 0.6546481251716614 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q5255048 |
| concepts[0].display_name | Demand response |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.5925005674362183 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C126255220 |
| concepts[2].level | 1 |
| concepts[2].score | 0.5596522688865662 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q141495 |
| concepts[2].display_name | Mathematical optimization |
| concepts[3].id | https://openalex.org/C206729178 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5529614686965942 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q2271896 |
| concepts[3].display_name | Scheduling (production processes) |
| concepts[4].id | https://openalex.org/C206658404 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5355010032653809 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q12725 |
| concepts[4].display_name | Electricity |
| concepts[5].id | https://openalex.org/C2780165032 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4426649808883667 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q16869822 |
| concepts[5].display_name | Energy consumption |
| concepts[6].id | https://openalex.org/C154982244 |
| concepts[6].level | 3 |
| concepts[6].score | 0.4351615905761719 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q5451844 |
| concepts[6].display_name | Firefly algorithm |
| concepts[7].id | https://openalex.org/C8880873 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4254068434238434 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q187787 |
| concepts[7].display_name | Genetic algorithm |
| concepts[8].id | https://openalex.org/C10558101 |
| concepts[8].level | 2 |
| concepts[8].score | 0.41145262122154236 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q689855 |
| concepts[8].display_name | Smart grid |
| concepts[9].id | https://openalex.org/C79403827 |
| concepts[9].level | 1 |
| concepts[9].score | 0.3773598372936249 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q3988 |
| concepts[9].display_name | Real-time computing |
| concepts[10].id | https://openalex.org/C171146098 |
| concepts[10].level | 1 |
| concepts[10].score | 0.333059698343277 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q124192 |
| concepts[10].display_name | Automotive engineering |
| concepts[11].id | https://openalex.org/C127413603 |
| concepts[11].level | 0 |
| concepts[11].score | 0.26291725039482117 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[11].display_name | Engineering |
| concepts[12].id | https://openalex.org/C85617194 |
| concepts[12].level | 2 |
| concepts[12].score | 0.15864261984825134 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q2072794 |
| concepts[12].display_name | Particle swarm optimization |
| concepts[13].id | https://openalex.org/C11413529 |
| concepts[13].level | 1 |
| concepts[13].score | 0.11830839514732361 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[13].display_name | Algorithm |
| concepts[14].id | https://openalex.org/C33923547 |
| concepts[14].level | 0 |
| concepts[14].score | 0.09361386299133301 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[14].display_name | Mathematics |
| concepts[15].id | https://openalex.org/C119599485 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q43035 |
| concepts[15].display_name | Electrical engineering |
| keywords[0].id | https://openalex.org/keywords/demand-response |
| keywords[0].score | 0.6546481251716614 |
| keywords[0].display_name | Demand response |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.5925005674362183 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/mathematical-optimization |
| keywords[2].score | 0.5596522688865662 |
| keywords[2].display_name | Mathematical optimization |
| keywords[3].id | https://openalex.org/keywords/scheduling |
| keywords[3].score | 0.5529614686965942 |
| keywords[3].display_name | Scheduling (production processes) |
| keywords[4].id | https://openalex.org/keywords/electricity |
| keywords[4].score | 0.5355010032653809 |
| keywords[4].display_name | Electricity |
| keywords[5].id | https://openalex.org/keywords/energy-consumption |
| keywords[5].score | 0.4426649808883667 |
| keywords[5].display_name | Energy consumption |
| keywords[6].id | https://openalex.org/keywords/firefly-algorithm |
| keywords[6].score | 0.4351615905761719 |
| keywords[6].display_name | Firefly algorithm |
| keywords[7].id | https://openalex.org/keywords/genetic-algorithm |
| keywords[7].score | 0.4254068434238434 |
| keywords[7].display_name | Genetic algorithm |
| keywords[8].id | https://openalex.org/keywords/smart-grid |
| keywords[8].score | 0.41145262122154236 |
| keywords[8].display_name | Smart grid |
| keywords[9].id | https://openalex.org/keywords/real-time-computing |
| keywords[9].score | 0.3773598372936249 |
| keywords[9].display_name | Real-time computing |
| keywords[10].id | https://openalex.org/keywords/automotive-engineering |
| keywords[10].score | 0.333059698343277 |
| keywords[10].display_name | Automotive engineering |
| keywords[11].id | https://openalex.org/keywords/engineering |
| keywords[11].score | 0.26291725039482117 |
| keywords[11].display_name | Engineering |
| keywords[12].id | https://openalex.org/keywords/particle-swarm-optimization |
| keywords[12].score | 0.15864261984825134 |
| keywords[12].display_name | Particle swarm optimization |
| keywords[13].id | https://openalex.org/keywords/algorithm |
| keywords[13].score | 0.11830839514732361 |
| keywords[13].display_name | Algorithm |
| keywords[14].id | https://openalex.org/keywords/mathematics |
| keywords[14].score | 0.09361386299133301 |
| keywords[14].display_name | Mathematics |
| language | en |
| locations[0].id | doi:10.3390/en11040888 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S198098182 |
| locations[0].source.issn | 1996-1073 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1996-1073 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Energies |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.mdpi.com/1996-1073/11/4/888/pdf?version=1525347960 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Energies |
| locations[0].landing_page_url | https://doi.org/10.3390/en11040888 |
| locations[1].id | pmh:oai:doaj.org/article:ec32759d9374497e9f081f6d82a6380b |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | cc-by-sa |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/cc-by-sa |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Energies, Vol 11, Iss 4, p 888 (2018) |
| locations[1].landing_page_url | https://doaj.org/article/ec32759d9374497e9f081f6d82a6380b |
| locations[2].id | pmh:oai:mdpi.com:/1996-1073/11/4/888/ |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306400947 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | True |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | MDPI (MDPI AG) |
| locations[2].source.host_organization | https://openalex.org/I4210097602 |
| locations[2].source.host_organization_name | Multidisciplinary Digital Publishing Institute (Switzerland) |
| locations[2].source.host_organization_lineage | https://openalex.org/I4210097602 |
| locations[2].license | cc-by |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | Text |
| locations[2].license_id | https://openalex.org/licenses/cc-by |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | Energies; Volume 11; Issue 4; Pages: 888 |
| locations[2].landing_page_url | https://dx.doi.org/10.3390/en11040888 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5075110510 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Zunaira Nadeem |
| authorships[0].countries | PK |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I929597975 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad 44000, Pakistan |
| authorships[0].institutions[0].id | https://openalex.org/I929597975 |
| authorships[0].institutions[0].ror | https://ror.org/03w2j5y17 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I929597975 |
| authorships[0].institutions[0].country_code | PK |
| authorships[0].institutions[0].display_name | National University of Sciences and Technology |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Zunaira Nadeem |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad 44000, Pakistan |
| authorships[1].author.id | https://openalex.org/A5055380996 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-3777-8249 |
| authorships[1].author.display_name | Nadeem Javaid |
| authorships[1].countries | PK |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I16076960 |
| authorships[1].affiliations[0].raw_affiliation_string | COMSATS Institute of Information Technology, Islamabad 44000, Pakistan |
| authorships[1].institutions[0].id | https://openalex.org/I16076960 |
| authorships[1].institutions[0].ror | https://ror.org/00nqqvk19 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I16076960 |
| authorships[1].institutions[0].country_code | PK |
| authorships[1].institutions[0].display_name | COMSATS University Islamabad |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Nadeem Javaid |
| authorships[1].is_corresponding | True |
| authorships[1].raw_affiliation_strings | COMSATS Institute of Information Technology, Islamabad 44000, Pakistan |
| authorships[2].author.id | https://openalex.org/A5067899667 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-3804-997X |
| authorships[2].author.display_name | Asad Waqar Malik |
| authorships[2].countries | PK |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I929597975 |
| authorships[2].affiliations[0].raw_affiliation_string | School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad 44000, Pakistan |
| authorships[2].institutions[0].id | https://openalex.org/I929597975 |
| authorships[2].institutions[0].ror | https://ror.org/03w2j5y17 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I929597975 |
| authorships[2].institutions[0].country_code | PK |
| authorships[2].institutions[0].display_name | National University of Sciences and Technology |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Asad Waqar Malik |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad 44000, Pakistan |
| authorships[3].author.id | https://openalex.org/A5036558418 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-5255-6532 |
| authorships[3].author.display_name | Sohail Iqbal |
| authorships[3].countries | PK |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I929597975 |
| authorships[3].affiliations[0].raw_affiliation_string | School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad 44000, Pakistan |
| authorships[3].institutions[0].id | https://openalex.org/I929597975 |
| authorships[3].institutions[0].ror | https://ror.org/03w2j5y17 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I929597975 |
| authorships[3].institutions[0].country_code | PK |
| authorships[3].institutions[0].display_name | National University of Sciences and Technology |
| authorships[3].author_position | last |
| authorships[3].raw_author_name | Sohail Iqbal |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad 44000, Pakistan |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.mdpi.com/1996-1073/11/4/888/pdf?version=1525347960 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2018-04-24T00:00:00 |
| display_name | Scheduling Appliances with GA, TLBO, FA, OSR and Their Hybrids Using Chance Constrained Optimization for Smart Homes |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10603 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 1.0 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2208 |
| primary_topic.subfield.display_name | Electrical and Electronic Engineering |
| primary_topic.display_name | Smart Grid Energy Management |
| related_works | https://openalex.org/W3005690382, https://openalex.org/W1589973774, https://openalex.org/W2353074559, https://openalex.org/W2171108821, https://openalex.org/W2023642087, https://openalex.org/W2074513827, https://openalex.org/W2402141875, https://openalex.org/W3125701662, https://openalex.org/W1498762745, https://openalex.org/W148990555 |
| cited_by_count | 56 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 6 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 5 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 8 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 5 |
| counts_by_year[4].year | 2021 |
| counts_by_year[4].cited_by_count | 13 |
| counts_by_year[5].year | 2020 |
| counts_by_year[5].cited_by_count | 4 |
| counts_by_year[6].year | 2019 |
| counts_by_year[6].cited_by_count | 8 |
| counts_by_year[7].year | 2018 |
| counts_by_year[7].cited_by_count | 7 |
| locations_count | 3 |
| best_oa_location.id | doi:10.3390/en11040888 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S198098182 |
| best_oa_location.source.issn | 1996-1073 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1996-1073 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Energies |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.mdpi.com/1996-1073/11/4/888/pdf?version=1525347960 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Energies |
| best_oa_location.landing_page_url | https://doi.org/10.3390/en11040888 |
| primary_location.id | doi:10.3390/en11040888 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S198098182 |
| primary_location.source.issn | 1996-1073 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1996-1073 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Energies |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/1996-1073/11/4/888/pdf?version=1525347960 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Energies |
| primary_location.landing_page_url | https://doi.org/10.3390/en11040888 |
| publication_date | 2018-04-10 |
| publication_year | 2018 |
| referenced_works | https://openalex.org/W2731610539, https://openalex.org/W1984560471, https://openalex.org/W1992679131, https://openalex.org/W2140778428, https://openalex.org/W2001386890, https://openalex.org/W1981657074, https://openalex.org/W2148544110, https://openalex.org/W1983820316, https://openalex.org/W1970154648, https://openalex.org/W1968167335, https://openalex.org/W2154397190, https://openalex.org/W2341715558, https://openalex.org/W2060148334, https://openalex.org/W2466555824, https://openalex.org/W2510963664, https://openalex.org/W2055134499, https://openalex.org/W2193157005, https://openalex.org/W2166121511, https://openalex.org/W2524244801, https://openalex.org/W2474466660, https://openalex.org/W2089486053, https://openalex.org/W2138785717, https://openalex.org/W1993474565, https://openalex.org/W2267281840, https://openalex.org/W2625867474, https://openalex.org/W2467667935, https://openalex.org/W2606833567, https://openalex.org/W2082361024, https://openalex.org/W6733530220, https://openalex.org/W2004584286, https://openalex.org/W2168160871, https://openalex.org/W2787445098, https://openalex.org/W2767423272, https://openalex.org/W2561740793, https://openalex.org/W1567888612, https://openalex.org/W2791838709, https://openalex.org/W2605864735, https://openalex.org/W2562650381, https://openalex.org/W2158547185, https://openalex.org/W2586852908 |
| referenced_works_count | 40 |
| abstract_inverted_index.a | 5, 170, 199, 226, 252 |
| abstract_inverted_index.In | 0, 106, 136, 209 |
| abstract_inverted_index.We | 75 |
| abstract_inverted_index.by | 67, 201 |
| abstract_inverted_index.in | 103 |
| abstract_inverted_index.is | 39, 180, 213, 236 |
| abstract_inverted_index.of | 35, 45, 72, 115, 198, 219, 225, 260 |
| abstract_inverted_index.on | 12 |
| abstract_inverted_index.to | 40, 83, 173, 190, 206, 215, 237 |
| abstract_inverted_index.we | 3, 58, 110, 140 |
| abstract_inverted_index.100 | 98 |
| abstract_inverted_index.20, | 96 |
| abstract_inverted_index.200 | 100 |
| abstract_inverted_index.50, | 97 |
| abstract_inverted_index.The | 32, 233 |
| abstract_inverted_index.and | 26, 53, 65, 89, 99, 125, 132, 159, 162, 204, 272, 275, 277 |
| abstract_inverted_index.are | 264 |
| abstract_inverted_index.for | 7, 95, 123, 157 |
| abstract_inverted_index.low | 181 |
| abstract_inverted_index.our | 119, 142 |
| abstract_inverted_index.the | 42, 69, 79, 107, 112, 137, 165, 174, 177, 184, 196, 217, 223, 230, 239, 244, 257, 268 |
| abstract_inverted_index.two | 104 |
| abstract_inverted_index.(FA) | 25 |
| abstract_inverted_index.This | 188 |
| abstract_inverted_index.also | 77 |
| abstract_inverted_index.base | 134, 186 |
| abstract_inverted_index.best | 70 |
| abstract_inverted_index.bill | 52 |
| abstract_inverted_index.cost | 193, 271, 276 |
| abstract_inverted_index.goal | 34 |
| abstract_inverted_index.have | 76 |
| abstract_inverted_index.home | 8, 116 |
| abstract_inverted_index.keep | 238 |
| abstract_inverted_index.load | 167, 227, 231 |
| abstract_inverted_index.peak | 82, 205 |
| abstract_inverted_index.rule | 29 |
| abstract_inverted_index.show | 150, 267 |
| abstract_inverted_index.that | 151 |
| abstract_inverted_index.they | 163 |
| abstract_inverted_index.this | 1, 37 |
| abstract_inverted_index.thus | 182 |
| abstract_inverted_index.used | 214 |
| abstract_inverted_index.user | 55, 90, 130, 200 |
| abstract_inverted_index.(GA), | 22 |
| abstract_inverted_index.(OSR) | 30 |
| abstract_inverted_index.based | 11 |
| abstract_inverted_index.below | 251 |
| abstract_inverted_index.cost, | 88 |
| abstract_inverted_index.delay | 203 |
| abstract_inverted_index.first | 108 |
| abstract_inverted_index.focus | 235 |
| abstract_inverted_index.helps | 189 |
| abstract_inverted_index.hence | 228 |
| abstract_inverted_index.homes | 102, 127, 161 |
| abstract_inverted_index.hours | 175 |
| abstract_inverted_index.major | 234 |
| abstract_inverted_index.power | 241, 245, 249 |
| abstract_inverted_index.price | 179 |
| abstract_inverted_index.shift | 164 |
| abstract_inverted_index.step, | 109, 139 |
| abstract_inverted_index.three | 60 |
| abstract_inverted_index.time) | 94 |
| abstract_inverted_index.time. | 279 |
| abstract_inverted_index.where | 176 |
| abstract_inverted_index.which | 266 |
| abstract_inverted_index.while | 48, 128, 194, 221, 247 |
| abstract_inverted_index.chance | 145 |
| abstract_inverted_index.demand | 168 |
| abstract_inverted_index.design | 4 |
| abstract_inverted_index.energy | 9, 43, 86, 273 |
| abstract_inverted_index.ensure | 216 |
| abstract_inverted_index.hybrid | 61, 121, 153 |
| abstract_inverted_index.level. | 255 |
| abstract_inverted_index.obtain | 111 |
| abstract_inverted_index.paper, | 2 |
| abstract_inverted_index.ratio, | 85 |
| abstract_inverted_index.ratio. | 208 |
| abstract_inverted_index.reduce | 41, 191 |
| abstract_inverted_index.second | 138 |
| abstract_inverted_index.single | 124, 158 |
| abstract_inverted_index.steps. | 105 |
| abstract_inverted_index.within | 243 |
| abstract_inverted_index.(TLBO), | 19 |
| abstract_inverted_index.OSR-FA, | 66 |
| abstract_inverted_index.OSR-GA, | 63 |
| abstract_inverted_index.average | 84, 207 |
| abstract_inverted_index.between | 270 |
| abstract_inverted_index.comfort | 91, 197 |
| abstract_inverted_index.desired | 80 |
| abstract_inverted_index.firefly | 23 |
| abstract_inverted_index.genetic | 20 |
| abstract_inverted_index.keeping | 248 |
| abstract_inverted_index.optimal | 27, 113 |
| abstract_inverted_index.policy. | 135, 187 |
| abstract_inverted_index.problem | 143 |
| abstract_inverted_index.propose | 59 |
| abstract_inverted_index.regions | 259 |
| abstract_inverted_index.results | 149 |
| abstract_inverted_index.schemes | 62, 122, 155 |
| abstract_inverted_index.sectors | 47 |
| abstract_inverted_index.theory. | 31 |
| abstract_inverted_index.through | 144 |
| abstract_inverted_index.waiting | 93, 278 |
| abstract_inverted_index.OSR-TLBO | 64 |
| abstract_inverted_index.comfort. | 56 |
| abstract_inverted_index.consumer | 166 |
| abstract_inverted_index.existing | 73 |
| abstract_inverted_index.feasible | 258 |
| abstract_inverted_index.features | 71 |
| abstract_inverted_index.multiple | 126, 160 |
| abstract_inverted_index.proposed | 152 |
| abstract_inverted_index.reducing | 49 |
| abstract_inverted_index.stopping | 28 |
| abstract_inverted_index.teaching | 16 |
| abstract_inverted_index.Moreover, | 256 |
| abstract_inverted_index.addition, | 210 |
| abstract_inverted_index.algorithm | 21, 24 |
| abstract_inverted_index.combining | 68 |
| abstract_inverted_index.designing | 36 |
| abstract_inverted_index.exceeding | 169 |
| abstract_inverted_index.following | 13, 183 |
| abstract_inverted_index.formulate | 141 |
| abstract_inverted_index.optimized | 78 |
| abstract_inverted_index.principal | 33 |
| abstract_inverted_index.smoothing | 229 |
| abstract_inverted_index.threshold | 133, 172, 185 |
| abstract_inverted_index.(appliance | 92 |
| abstract_inverted_index.Simulation | 148 |
| abstract_inverted_index.acceptable | 254 |
| abstract_inverted_index.appliances | 117, 220, 240, 261 |
| abstract_inverted_index.calculated | 265 |
| abstract_inverted_index.controller | 6, 38 |
| abstract_inverted_index.management | 10 |
| abstract_inverted_index.maximizing | 54 |
| abstract_inverted_index.minimizing | 202 |
| abstract_inverted_index.predefined | 171 |
| abstract_inverted_index.scheduling | 114, 154, 218 |
| abstract_inverted_index.algorithms. | 74 |
| abstract_inverted_index.algorithms: | 15 |
| abstract_inverted_index.considering | 129, 195, 222 |
| abstract_inverted_index.constrained | 146 |
| abstract_inverted_index.constraint, | 246 |
| abstract_inverted_index.consumption | 44, 242, 250, 263, 274 |
| abstract_inverted_index.electricity | 51, 178, 192, 262 |
| abstract_inverted_index.parameters: | 81 |
| abstract_inverted_index.pre-defined | 253 |
| abstract_inverted_index.preferences | 131 |
| abstract_inverted_index.residential | 46 |
| abstract_inverted_index.consumer’s | 50 |
| abstract_inverted_index.consumption, | 87 |
| abstract_inverted_index.curtailment. | 232 |
| abstract_inverted_index.implementing | 118 |
| abstract_inverted_index.optimization | 18, 212 |
| abstract_inverted_index.outperformed | 156 |
| abstract_inverted_index.relationship | 269 |
| abstract_inverted_index.Additionally, | 57 |
| abstract_inverted_index.heterogeneous | 101 |
| abstract_inverted_index.optimization. | 147 |
| abstract_inverted_index.uncertainties | 224 |
| abstract_inverted_index.aforementioned | 120 |
| abstract_inverted_index.learning-based | 17 |
| abstract_inverted_index.meta-heuristic | 14 |
| abstract_inverted_index.chance-constrained | 211 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5055380996 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I16076960 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.9100000262260437 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.94599235 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |