DECODE: Data-driven Energy Consumption Prediction leveraging Historical
Data and Environmental Factors in Buildings
- URL: http://arxiv.org/abs/2309.02908v2
- Date: Tue, 6 Feb 2024 15:37:11 GMT
- Title: DECODE: Data-driven Energy Consumption Prediction leveraging Historical
Data and Environmental Factors in Buildings
- Authors: Aditya Mishra, Haroon R. Lone, Aayush Mishra
- Abstract summary: This paper introduces a Long Short-Term Memory (LSTM) model designed to forecast building energy consumption.
The LSTM model provides accurate short, medium, and long-term energy predictions for residential and commercial buildings.
It demonstrates exceptional prediction accuracy, boasting the highest R2 score of 0.97 and the most favorable mean absolute error (MAE) of 0.007.
- Score: 1.2891210250935148
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Energy prediction in buildings plays a crucial role in effective energy
management. Precise predictions are essential for achieving optimal energy
consumption and distribution within the grid. This paper introduces a Long
Short-Term Memory (LSTM) model designed to forecast building energy consumption
using historical energy data, occupancy patterns, and weather conditions. The
LSTM model provides accurate short, medium, and long-term energy predictions
for residential and commercial buildings compared to existing prediction
models. We compare our LSTM model with established prediction methods,
including linear regression, decision trees, and random forest. Encouragingly,
the proposed LSTM model emerges as the superior performer across all metrics.
It demonstrates exceptional prediction accuracy, boasting the highest R2 score
of 0.97 and the most favorable mean absolute error (MAE) of 0.007. An
additional advantage of our developed model is its capacity to achieve
efficient energy consumption forecasts even when trained on a limited dataset.
We address concerns about overfitting (variance) and underfitting (bias)
through rigorous training and evaluation on real-world data. In summary, our
research contributes to energy prediction by offering a robust LSTM model that
outperforms alternative methods and operates with remarkable efficiency,
generalizability, and reliability.
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