Deep Learning Neural Network Prediction System Enhanced with Best Window Size in Sliding Window Algorithm for Predicting Domestic Power Consumption in a Residential Building Article Swipe
Dimpal Tomar
,
Pradeep Tomar
,
Arpit Bhardwaj
,
G. R. Sinha
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/7216959
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/7216959
Buildings are considered to be one of the world’s largest consumers of energy. The productive utilization of energy will spare the accessible energy assets for the following ages. In this paper, we analyze and predict the domestic electric power consumption of a single residential building, implementing deep learning approach (LSTM and CNN). In these models, a novel feature is proposed, the “best N window size” that will focus on identifying the reliable time period in the past data, which yields an optimal prediction model for domestic energy consumption known as deep learning recurrent neural network prediction system with improved sliding window algorithm. The proposed prediction system is tuned to achieve high accuracy based on various hyperparameters. This work performs a comparative study of different variations of the deep learning model and records the best Root Mean Square Error value compared to other learning models for the benchmark energy consumption dataset.
Related Topics
Concepts
Sliding window protocol
Window (computing)
Computer science
Power consumption
Artificial neural network
Artificial intelligence
Consumption (sociology)
Deep learning
Algorithm
Power (physics)
Window of opportunity
Predictive power
Machine learning
Pattern recognition (psychology)
Real-time computing
Operating system
Philosophy
Epistemology
Social science
Physics
Sociology
Quantum mechanics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2022/7216959
- https://downloads.hindawi.com/journals/cin/2022/7216959.pdf
- OA Status
- hybrid
- Cited By
- 24
- References
- 61
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4214879862
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4214879862Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2022/7216959Digital Object Identifier
- Title
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Deep Learning Neural Network Prediction System Enhanced with Best Window Size in Sliding Window Algorithm for Predicting Domestic Power Consumption in a Residential BuildingWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-03-02Full publication date if available
- Authors
-
Dimpal Tomar, Pradeep Tomar, Arpit Bhardwaj, G. R. SinhaList of authors in order
- Landing page
-
https://doi.org/10.1155/2022/7216959Publisher landing page
- PDF URL
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https://downloads.hindawi.com/journals/cin/2022/7216959.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
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https://downloads.hindawi.com/journals/cin/2022/7216959.pdfDirect OA link when available
- Concepts
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Sliding window protocol, Window (computing), Computer science, Power consumption, Artificial neural network, Artificial intelligence, Consumption (sociology), Deep learning, Algorithm, Power (physics), Window of opportunity, Predictive power, Machine learning, Pattern recognition (psychology), Real-time computing, Operating system, Philosophy, Epistemology, Social science, Physics, Sociology, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
24Total citation count in OpenAlex
- Citations by year (recent)
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2025: 6, 2024: 8, 2023: 5, 2022: 5Per-year citation counts (last 5 years)
- References (count)
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61Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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