FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386753409> ?p ?o ?g. }

• W4386753409 endingPage "121545" @default.
• W4386753409 startingPage "121545" @default.
• W4386753409 abstract "Building energy prediction and management has become increasingly important in recent decades, driven by the growth of Internet of Things (IoT) devices and the availability of more energy data. However, energy data is often collected from multiple sources and can be incomplete or inconsistent, which can hinder accurate predictions and management of energy systems and limit the usefulness of the data for decision-making and research. To address this issue, past studies have focused on imputing missing gaps in energy data, including random and continuous gaps. One of the main challenges in this area is the lack of validation on a benchmark dataset with various building and meter types, making it difficult to accurately evaluate the performance of different imputation methods. Another challenge is the lack of application of state-of-the-art imputation methods for missing gaps in energy data. Contemporary image-inpainting methods, such as Partial Convolution (PConv), have been widely used in the computer vision domain and have demonstrated their effectiveness in dealing with complex missing patterns. Given that energy data often exhibits regular daily or weekly patterns, such methods could be leveraged to exploit the regularity of the data to learn underlying patterns and generate more accurate predictions for missing values. To study whether energy data imputation can benefit from the image-based deep learning method, this study compared PConv, Convolutional neural networks (CNNs), and weekly persistence method using one of the biggest publicly available whole building energy datasets, consisting of 1479 power meters worldwide, as the benchmark. The results show that, compared to the CNN with the raw time series (1D-CNN) and the weekly persistence method, neural network models with reshaped energy data with two dimensions reduced the Mean Squared Error (MSE) by 10% to 30%. The advanced deep learning method, Partial convolution (PConv), has further reduced the MSE by 20%–30% than 2D-CNN and stands out among all models. Based on these results, this study demonstrates the potential applicability of time-series imaging in imputing energy data. The proposed imputation model has also been tested on a benchmark dataset with a range of meter types and sources, demonstrating its generalizability to include additional accessible energy datasets. This offers a scalable and effective solution for filling in missing energy data in both academic and industrial contexts." @default.
• W4386753409 created "2023-09-15" @default.
• W4386753409 creator A5005453560 @default.
• W4386753409 creator A5034300222 @default.
• W4386753409 creator A5045303713 @default.
• W4386753409 creator A5071874699 @default.
• W4386753409 date "2024-01-01" @default.
• W4386753409 modified "2023-10-02" @default.
• W4386753409 title "Filling time-series gaps using image techniques: Multidimensional context autoencoder approach for building energy data imputation" @default.
• W4386753409 cites W2003951468 @default.
• W4386753409 cites W2029725686 @default.
• W4386753409 cites W2068708047 @default.
• W4386753409 cites W2081546775 @default.
• W4386753409 cites W2123234956 @default.
• W4386753409 cites W2180271301 @default.
• W4386753409 cites W2769174254 @default.
• W4386753409 cites W2908587984 @default.
• W4386753409 cites W2948293367 @default.
• W4386753409 cites W2963420272 @default.
• W4386753409 cites W3008646003 @default.
• W4386753409 cites W3011632114 @default.
• W4386753409 cites W3012214319 @default.
• W4386753409 cites W3023969710 @default.
• W4386753409 cites W3027972301 @default.
• W4386753409 cites W3041657070 @default.
• W4386753409 cites W3043547428 @default.
• W4386753409 cites W3046296398 @default.
• W4386753409 cites W3080957353 @default.
• W4386753409 cites W3085770583 @default.
• W4386753409 cites W3086340023 @default.
• W4386753409 cites W3094252156 @default.
• W4386753409 cites W3102846047 @default.
• W4386753409 cites W3105183525 @default.
• W4386753409 cites W3112325088 @default.
• W4386753409 cites W3132473200 @default.
• W4386753409 cites W3136501109 @default.
• W4386753409 cites W3173615095 @default.
• W4386753409 cites W3174700462 @default.
• W4386753409 cites W3188373407 @default.
• W4386753409 cites W3200723581 @default.
• W4386753409 cites W3203538104 @default.
• W4386753409 cites W3209725881 @default.
• W4386753409 cites W3210978987 @default.
• W4386753409 cites W4224092281 @default.
• W4386753409 cites W4238385053 @default.
• W4386753409 cites W4282977571 @default.
• W4386753409 cites W4283261688 @default.
• W4386753409 cites W4283826931 @default.
• W4386753409 cites W4288045457 @default.
• W4386753409 cites W4310882134 @default.
• W4386753409 cites W4312497550 @default.
• W4386753409 cites W4317756824 @default.
• W4386753409 cites W4376127284 @default.
• W4386753409 doi "https://doi.org/10.1016/j.applthermaleng.2023.121545" @default.
• W4386753409 hasPublicationYear "2024" @default.
• W4386753409 type Work @default.
• W4386753409 citedByCount "0" @default.
• W4386753409 crossrefType "journal-article" @default.
• W4386753409 hasAuthorship W4386753409A5005453560 @default.
• W4386753409 hasAuthorship W4386753409A5034300222 @default.
• W4386753409 hasAuthorship W4386753409A5045303713 @default.
• W4386753409 hasAuthorship W4386753409A5071874699 @default.
• W4386753409 hasConcept C101738243 @default.
• W4386753409 hasConcept C108583219 @default.
• W4386753409 hasConcept C119857082 @default.
• W4386753409 hasConcept C124101348 @default.
• W4386753409 hasConcept C13280743 @default.
• W4386753409 hasConcept C154945302 @default.
• W4386753409 hasConcept C185798385 @default.
• W4386753409 hasConcept C205649164 @default.
• W4386753409 hasConcept C41008148 @default.
• W4386753409 hasConcept C58041806 @default.
• W4386753409 hasConcept C9357733 @default.
• W4386753409 hasConceptScore W4386753409C101738243 @default.
• W4386753409 hasConceptScore W4386753409C108583219 @default.
• W4386753409 hasConceptScore W4386753409C119857082 @default.
• W4386753409 hasConceptScore W4386753409C124101348 @default.
• W4386753409 hasConceptScore W4386753409C13280743 @default.
• W4386753409 hasConceptScore W4386753409C154945302 @default.
• W4386753409 hasConceptScore W4386753409C185798385 @default.
• W4386753409 hasConceptScore W4386753409C205649164 @default.
• W4386753409 hasConceptScore W4386753409C41008148 @default.
• W4386753409 hasConceptScore W4386753409C58041806 @default.
• W4386753409 hasConceptScore W4386753409C9357733 @default.
• W4386753409 hasFunder F4320320751 @default.
• W4386753409 hasLocation W43867534091 @default.
• W4386753409 hasOpenAccess W4386753409 @default.
• W4386753409 hasPrimaryLocation W43867534091 @default.
• W4386753409 hasRelatedWork W2541565311 @default.
• W4386753409 hasRelatedWork W2669956259 @default.
• W4386753409 hasRelatedWork W2784019465 @default.
• W4386753409 hasRelatedWork W2939353110 @default.
• W4386753409 hasRelatedWork W3049453136 @default.
• W4386753409 hasRelatedWork W3206517600 @default.
• W4386753409 hasRelatedWork W4214497138 @default.
• W4386753409 hasRelatedWork W4229025315 @default.
• W4386753409 hasRelatedWork W4380075502 @default.
• W4386753409 hasRelatedWork W4382406460 @default.

• next