JITA
JITA Journal of Information Technology and Applications
This paper presents a machine-learning-based approach for short-term forecasting of household electricity consumption. The study aims to model temporal consumption patterns and support intelligent energy management in residential environments. Historical power consumption data were collected, cleaned, normalized and transformed into supervised learning sequences using sliding window techniques. A Long Short-Term Memory (LSTM) neural network was developed to capture time-dependent characteristics of electricity usage. The model was trained using the Adam optimization algorithm and evaluated using standard regression metrics, including Mean Absolute Error (MAE), which indicated high prediction accuracy and robustness. To ensure practical applicability, the proposed system integrates edge computing principles. Experimental results demonstrate that deep learning-based time-series forecasting can effectively predict short-term energy consumption. The proposed approach contributes to smart home energy monitoring by providing a scalable, efficient and reliable solution, and supports sustainable electricity usage through data-driven decision-making. The findings highlight the importance of integrating predictive analytics into future intelligent energy systems.
Considering established relevance to the GTSRB dataset, it is important to emphasize that research investigates the effectiveness of convolutional neural networks (CNN) in the field of road sign recognition. Following that wide range of techniques for comprehensive preprocessing pipelines were implemented, including data normalization and augmentation as well as resizing images. The CNN model has demonstrated the ability to overcome adverse conditions across multiple road sign classes, demonstrating outstanding scores against the performance metrics used in testing and evaluation process. Model achieved classification accuracies exceeding 99% across most categories. Nevertheless, in certain classes there is presence of performance metric decline related to the inaccurate visualization and contradiction of features. The crucial role of the preprocessing phase has been highlighted while the implementation of the CNN model has been identified as one of the most reliable approaches in the field of road sign recognition. However future implications must be considered to achieve the full potential of the model. Some of the crucial contributions for the future will be introducing real life variation in the dataset. On the other hand, occlusion, lighting and weather conditions are the important factors that should be brought into focus.
Considering established relevance to the GTSRB dataset, it is important to emphasize that research investigates the effectiveness of convolutional neural networks (CNN) in the field of road sign recognition. Following that wide range of techniques for comprehensive preprocessing pipelines were implemented, including data normalization and augmentation as well as resizing images. The CNN model has demonstrated the ability to overcome adverse conditions across multiple road sign classes, demonstrating outstanding scores against the performance metrics used in testing and evaluation process. Model achieved classification accuracies exceeding 99% across most categories. Nevertheless, in certain classes there is presence of performance metric decline related to the inaccurate visualization and contradiction of features. The crucial role of the preprocessing phase has been highlighted while the implementation of the CNN model has been identified as one of the most reliable approaches in the field of road sign recognition. However future implications must be considered to achieve the full potential of the model. Some of the crucial contributions for the future will be introducing real life variation in the dataset. On the other hand, occlusion, lighting and weather conditions are the important factors that should be brought into focus.