FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4213444802> ?p ?o ?g. }

• W4213444802 endingPage "1444" @default.
• W4213444802 startingPage "1427" @default.
• W4213444802 abstract "Electricity, being the most efficient secondary energy, contributes for a larger proportion of overall energy usage. Due to a lack of storage for energy resources, over supply will result in energy dissipation and substantial investment waste. Accurate electricity consumption prediction is vital because it allows for the preparation of potential power generation systems to satisfy the growing demands for electrical energy as well as: smart distributed grids, assessing the degree of socioeconomic growth, distributed system design, tariff plans, demand-side management, power generation planning, and providing electricity supply stability by balancing the amount of electricity produced and consumed. This paper proposes a medium-term prediction model that can predict electricity consumption for a given location in Saudi Arabia. Hence, this study implemented a standalone Artificial Neural Network (ANN) model and bagging ensemble for predicting total monthly electricity consumption in 18 locations across Saudi Arabia. The dataset used in this research is gathered exclusively from the Saudi Electric Company. The pre-processing phase included normalizing the data using min-max method and mapping the cyclical attribute to its sine and cosine facets. The number of neurons and learning rate of the standalone model were optimized using hyperparameter tuning. Finally, the standalone model was tested against the bagging ensemble using the optimized ANN. The bagging ensemble with an optimized ANN as the chosen classifier outperformed the standalone ANN model. The results for the proposed model produced 0.9116 Correlation Coefficient (CC), 0.2836 Mean Absolute Percentage Error (MAPE), 0.4578, Root Mean Squared Percentage Error (RMSPE), 0.0298 MAE, and 0.069 Root Mean Squared Error (RMSE), respectively." @default.
• W4213444802 created "2022-02-25" @default.
• W4213444802 creator A5063706331 @default.
• W4213444802 creator A5066550612 @default.
• W4213444802 date "2022-01-01" @default.
• W4213444802 modified "2023-09-26" @default.
• W4213444802 title "Machine Learning Empowered Electricity Consumption Prediction" @default.
• W4213444802 cites W2006846066 @default.
• W4213444802 cites W2030001930 @default.
• W4213444802 cites W2053555702 @default.
• W4213444802 cites W2057156516 @default.
• W4213444802 cites W2074993972 @default.
• W4213444802 cites W2088603690 @default.
• W4213444802 cites W2089217930 @default.
• W4213444802 cites W2152761983 @default.
• W4213444802 cites W2168138569 @default.
• W4213444802 cites W2295560611 @default.
• W4213444802 cites W2394990251 @default.
• W4213444802 cites W2745362263 @default.
• W4213444802 cites W2754141826 @default.
• W4213444802 cites W2763749451 @default.
• W4213444802 cites W2765616368 @default.
• W4213444802 cites W2788001476 @default.
• W4213444802 cites W2920927670 @default.
• W4213444802 cites W2945037218 @default.
• W4213444802 cites W2949516426 @default.
• W4213444802 cites W2950877184 @default.
• W4213444802 cites W2973940591 @default.
• W4213444802 cites W2982236294 @default.
• W4213444802 cites W2997318637 @default.
• W4213444802 cites W3038178920 @default.
• W4213444802 cites W3039412945 @default.
• W4213444802 cites W3045387762 @default.
• W4213444802 cites W3045433138 @default.
• W4213444802 cites W3086569861 @default.
• W4213444802 cites W3118252934 @default.
• W4213444802 cites W3121067595 @default.
• W4213444802 cites W3129064270 @default.
• W4213444802 cites W3134417437 @default.
• W4213444802 cites W3150593357 @default.
• W4213444802 cites W3156595259 @default.
• W4213444802 cites W3166741258 @default.
• W4213444802 cites W3214296457 @default.
• W4213444802 cites W4212883601 @default.
• W4213444802 cites W4248510537 @default.
• W4213444802 doi "https://doi.org/10.32604/cmc.2022.025722" @default.
• W4213444802 hasPublicationYear "2022" @default.
• W4213444802 type Work @default.
• W4213444802 citedByCount "2" @default.
• W4213444802 countsByYear W42134448022022 @default.
• W4213444802 countsByYear W42134448022023 @default.
• W4213444802 crossrefType "journal-article" @default.
• W4213444802 hasAuthorship W4213444802A5063706331 @default.
• W4213444802 hasAuthorship W4213444802A5066550612 @default.
• W4213444802 hasBestOaLocation W42134448021 @default.
• W4213444802 hasConcept C105795698 @default.
• W4213444802 hasConcept C119599485 @default.
• W4213444802 hasConcept C127413603 @default.
• W4213444802 hasConcept C139945424 @default.
• W4213444802 hasConcept C150217764 @default.
• W4213444802 hasConcept C154945302 @default.
• W4213444802 hasConcept C206658404 @default.
• W4213444802 hasConcept C2780165032 @default.
• W4213444802 hasConcept C33923547 @default.
• W4213444802 hasConcept C41008148 @default.
• W4213444802 hasConcept C45942800 @default.
• W4213444802 hasConcept C50644808 @default.
• W4213444802 hasConceptScore W4213444802C105795698 @default.
• W4213444802 hasConceptScore W4213444802C119599485 @default.
• W4213444802 hasConceptScore W4213444802C127413603 @default.
• W4213444802 hasConceptScore W4213444802C139945424 @default.
• W4213444802 hasConceptScore W4213444802C150217764 @default.
• W4213444802 hasConceptScore W4213444802C154945302 @default.
• W4213444802 hasConceptScore W4213444802C206658404 @default.
• W4213444802 hasConceptScore W4213444802C2780165032 @default.
• W4213444802 hasConceptScore W4213444802C33923547 @default.
• W4213444802 hasConceptScore W4213444802C41008148 @default.
• W4213444802 hasConceptScore W4213444802C45942800 @default.
• W4213444802 hasConceptScore W4213444802C50644808 @default.
• W4213444802 hasIssue "1" @default.
• W4213444802 hasLocation W42134448021 @default.
• W4213444802 hasOpenAccess W4213444802 @default.
• W4213444802 hasPrimaryLocation W42134448021 @default.
• W4213444802 hasRelatedWork W2059652129 @default.
• W4213444802 hasRelatedWork W2576572654 @default.
• W4213444802 hasRelatedWork W2778123278 @default.
• W4213444802 hasRelatedWork W2807954395 @default.
• W4213444802 hasRelatedWork W3040646013 @default.
• W4213444802 hasRelatedWork W3140747433 @default.
• W4213444802 hasRelatedWork W3173604411 @default.
• W4213444802 hasRelatedWork W3178441567 @default.
• W4213444802 hasRelatedWork W4307940504 @default.
• W4213444802 hasRelatedWork W4383908742 @default.
• W4213444802 hasVolume "72" @default.
• W4213444802 isParatext "false" @default.
• W4213444802 isRetracted "false" @default.
• W4213444802 workType "article" @default.