In this paper, detailed scalability and replicability plans have been developed to facilitate the adoption of innovation technologies in the pan-EU market. Smart grid development must enable both information and power exchange between suppliers and customers, thanks to the enormous innovation in intelligent communication, monitoring, and management systems. Implementing physical infrastructure alone is not enough, but a smart grid must include new business models and new regulations. In recent years, the number, participants, and scope of smart grid initiatives have increased, with different goals and results. FLEXITRANSTORE project integrates hardware and software solutions in all areas of the transmission system and wholesale markets, unleashing the potential for full flexibility of power systems and promoting the penetration of renewable energy sources and pan-EU markets. Full deployment of these demonstrated solutions requires a reasonable level of scalability and replicability to prevent project demonstrators from continuing local experimental exercises. Scalability and replicability are fundamental requirements for successful scaling-up and replication. Therefore, scalability and replicability enable or at least reduce barriers to the growth and reuse of project demonstrator results.

The purpose of this study is to investigate the potential of AI technology in developing a decision support system that can improve the effectiveness of wireless sensor networks (WSNs) in e-commerce, specifically in enhancing the features of consumer electronics. This research project is focused on optimizing wireless sensor networks for e-commerce consumer electronics by incorporating AI-based decision support systems. The primary objective of this study is to enhance energy efficiency and performance in online shopping platforms. Various algorithms and methodologies are proposed and assessed, including Adaptive Clustering, the Path Selection Algorithm, Fuzzy Logic-Controlled Energy Management, the Genetic Algorithm for Resource Allocation, and Deep Sleep Scheduling. These techniques improve network efficiency and reduce power consumption in e-commerce applications. The study demonstrates that integrating AI in consumer electronics can result in a remarkable 40% increase in energy efficiency. Comparative analyses conducted through simulations and real-world assessments indicate that the proposed methodology outperforms traditional techniques by 35%. This research underscores the vital role of AI in enhancing network performance and energy efficiency in e-commerce. The results suggest that implementing AI-driven strategies in wireless sensor networks for consumer electronics can significantly improve online shopping experiences. AI-based decision support systems can optimize wireless sensor networks for consumer electronics, improving energy efficiency and network performance on online shopping platforms.

Designers are required to plan for future expansion and also to estimate the grid’s future utilization. This means that an effective modeling and forecasting technique, which will use efficiently the...

In coastal areas, coconuts are a common crop. Everyone from farmers to lawmakers and businesses would benefit from an accurate forecast of coconut production. Internet of Things (IoT) sensors are strategically positioned to continuously monitor the environment and gather production statistics to obtain accurate agricultural output predictions. To effectively estimate coconut prediction, this study presents an enhanced deep learning classifier called Bi-directional Long Short-Term Memory (BILSTM) with the integrated Lévy Flight and Seagull Optimization Algorithm (LFSOA). LASSO feature selection is applied to eliminate the superfluous characteristics in the yield estimation. To further enhance the coconut yield estimate, the optimal set of hyperparameters for BILSTM is tuned by the LFSOA, which helps to avoid the overfitting issue. For the results, the BILSTM is compared against different classifiers such as Recurrent Neural Network (RNN), Random Forest Classifier (RFC), and LSTM. Similarly, LFSOA-based hyperparameter tuning is contrasted with different optimization algorithms. The outputs show that LFSOA-based hyperparameter tuning in BILSTM achieved accuracy, precision, recall, and f1-score of 98.963%, 99.026%, 99.155%, and 95.758%, respectively, which are higher when compared to existing methods. Similarly, the BILSTM-LFSOA accomplished better results in statistical measures, including the Root Mean Square Error (RMSE) of 0.105, Mean Squared Error (MSE) of 0.011, Mean Absolute Error (MAE) of 0.094, and coefficient of determination (R²) of 0.954, respectively. From the overall analysis, the proposed BILSTM-LFSOA improves coconut yield prediction by achieving better results in all the performance measures when compared with existing models. The results of this study are important to many stakeholders, including but not limited to policymakers, farmers, banks, and insurance companies. As coconuts are an important crop in developing countries, accurate coconut yield forecasting will lead to greater financial and food security in these regions.