Interrelation between Temporal Coordinates and Intrusion Detection Techniques in Cyber Physical Systems

Jacob, Stalin; Balanagireddy, G; Kumar, K. Suresh; Vijay, M. M.

doi:10.1109/icict54344.2022.9850633

2022 International Conference on Inventive Computation Technologies (ICICT) 2022

DOI: 10.1109/icict54344.2022.9850633

|View full text |Cite

Interrelation between Temporal Coordinates and Intrusion Detection Techniques in Cyber Physical Systems

Stalin Jacob

G Balanagireddy

K. Suresh Kumar

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2025

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Prediction of Electricity Consumption in Residential Areas using Temporal Fusion Transformer and Convolutional Neural Network

B N,

K S

2025

JMC

View full text Add to dashboard Cite

The consumption of energy in the residential area causes adverse impacts on the environment. The mitigation or maintenance of power consumption can be the main step to preserve electricity for the future and proper supply. In context with this, the work focuses on predicting the consumption of energy with the novel hybrid tactic. The hybrid tactic is the integration of Temporal Fusion Transformer (TFT) and Convolutional Neural Network (CNN) (HTFT-CNN). This work is developed to predict the usage of energy across varying time frames with the grip of a multivariate time series of the power consumption of individual residential areas. The proposed HTFT-CNN is implemented to combine both the feature and temporal-based data and can be utilized to observe intricate consumption patterns. The Attention mechanism (AM) is implemented for the fusion of features that are obtained using the proposed HTFT-CNN tactic. The multi-step (k=24) for input sequences and k=24 is the length of input sequence at 24 hours. Simulations are conducted to analyze the robustness and forecasting accuracy of the designed model with the parameters such as Root Mean Square error (RMSE), and Mean Absolute Percentage Error (MAPE). The analyzed performance depicts that the proposed design can be used for planning and energy management in the residential area with minimized RMSE and MAPE values.

show abstract

Prediction of Electricity Consumption in Residential Areas using Temporal Fusion Transformer and Convolutional Neural Network

B N,

K S

2025

JMC

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Interrelation between Temporal Coordinates and Intrusion Detection Techniques in Cyber Physical Systems

Cited by 1 publication

References 20 publications

Prediction of Electricity Consumption in Residential Areas using Temporal Fusion Transformer and Convolutional Neural Network

Prediction of Electricity Consumption in Residential Areas using Temporal Fusion Transformer and Convolutional Neural Network

Contact Info

Product

Resources

About