Smart City Gnosys
Smart city article details
| Title | Energy Consumption Prediction Using Modified Deep Cnn-Bi Lstm With Attention Mechanism |
|---|---|
| ID_Doc | 23183 |
| Authors | Binbusayyis A.; Sha M. |
| Year | 2025 |
| Published | Heliyon, 11, 1 |
| DOI | http://dx.doi.org/10.1016/j.heliyon.2024.e41507 |
| Abstract | The prediction of energy consumption in households is essential due to the reliance on electrical appliances for daily activities. Accurate assessment of energy demand is crucial for effective energy generation, preventing overloads and optimizing energy storage. Traditional techniques have limitations in accuracy and error rates, necessitating advancements in prediction techniques. To enhance prediction accuracy, a proposed smart city system utilizes the Household Energy Consumption dataset, employing deep learning algorithms. In the beginning, data pre-processing addresses missing values and performs feature scaling for normalizing independent variables. Followed by that, Modified Deep CNN-Bi-LSTM (Convolutional Neural Network and Bi-directional Long Short Term Memory) with attention mechanism is utilized for regression which extracts temporal and spatial complex features. Deep CNN extracts features impacting energy consumption whereas Bi-LSTM with attention layer finds suitability for regression as it is capable of modelling irregular trends in the time-series components, where the attention mechanism is implemented to enhance the decoder's ability to selectively focus on the most relevant segments of the input sequence. This is achieved through a weighted integration of all encoded input trajectories, allowing the model to dynamically emphasize the vectors that carry the highest significance for accurate predictions. Based on regression outcomes from analysis taken in hourly, daily and monthly time intervals, enhanced prediction accuracy is estimated through evaluation metrics such as MSE (Mean Square Error), MAPE (Mean Absolute Percentage Error) and RMSE (Root Mean Square Error) which determines the efficacy of the system, where Specifically, the proposed model achieves MSE of 0.123, MAE of 0.22, and MAPE of 324.12. Furthermore, this model demonstrates a training time of 692.12 s and a prediction time of just 1.87 s. Therefore, present research highlights the critical need for accurate energy consumption prediction in households, driven by the increasing reliance on electrical appliances in daily life. © 2025 The Authors |
| Author Keywords | Bi directional long short term memory; Convolutional neural network; Deep learning; Energy consumption and household energy dataset |
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