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Related Experiment Video

Updated: Sep 30, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Deep Learning Neural Network Prediction System Enhanced with Best Window Size in Sliding Window Algorithm for

Dimpal Tomar¹, Pradeep Tomar¹, Arpit Bhardwaj²

¹Gautam Buddha University, Greater Noida, UP, India.

Computational Intelligence and Neuroscience

|March 14, 2022

Summary

This summary is machine-generated.

This study predicts residential energy consumption using deep learning (LSTM and CNN) with a novel "best N window size" feature. The proposed system achieves high accuracy, outperforming other models for energy demand prediction.

Related Experiment Videos

Last Updated: Sep 30, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Area of Science:

Energy Science
Artificial Intelligence
Building Science

Background:

Buildings are major global energy consumers, necessitating efficient energy management.
Sustainable energy utilization is crucial for future generations.
Accurate prediction of domestic electric power consumption is vital for grid stability and resource management.

Purpose of the Study:

To analyze and predict domestic electric power consumption in a residential building.
To implement and evaluate deep learning models, specifically Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN).
To introduce and validate a "best N window size" feature for optimizing energy consumption prediction.

Main Methods:

Utilized deep learning approaches, including LSTM and CNN, for time-series energy consumption forecasting.
Developed a novel "best N window size" feature to identify optimal historical data periods for prediction.
Implemented a deep learning recurrent neural network prediction system with an improved sliding window algorithm.
Tuned prediction system hyperparameters to maximize accuracy and performance.

Main Results:

Achieved high accuracy in predicting domestic energy consumption using the proposed deep learning models.
The "best N window size" feature significantly improved the reliability and accuracy of the prediction model.
Demonstrated superior performance by recording the best Root Mean Square Error (RMSE) value compared to other learning models on a benchmark dataset.

Conclusions:

The proposed deep learning recurrent neural network prediction system with the "best N window size" is effective for accurate domestic energy consumption forecasting.
The integration of LSTM and CNN with the novel feature offers a robust solution for energy demand prediction.
This approach contributes to efficient energy management in residential buildings, supporting energy conservation efforts.