Long-term energy consumption forecasting plays a crucial role in the effective management of smart grids , especially with the increasing integration of renewable energy sources and distributed energy resources (DERs). Accurate predictions are essential for optimizing grid operations, balancing supply and demand, and ensuring stability. This paper proposes a hybrid Long Short-Term Memory (LSTM) and XGBoost model for long-term energy consumption forecasting in smart grids. The LSTM component captures the temporal dependencies in energy consumption patterns, while the XGBoost component enhances forecasting accuracy through gradient-boosted decision trees. The hybrid model is evaluated using real-world data from smart grids, and its performance is assessed based on key forecasting accuracy metrics. Experimental results demonstrate that the hybrid LSTM-XGBoost model provides superior predictive performance, reducing forecast errors and offering a more reliable approach for long-term energy consumption predictions. This approach shows significant potential for improving energy management in smart grids, enabling better resource allocation and reducing energy consumption .

Efficient management of agricultural water resources has become increasingly critical due to climate variability and rising global food demand. This paper presents a comprehensive IoT-based system for real-time agricultural water forecasting, integrating field-deployed sensors, cloud infrastructure, and advanced machine learning models. The system automates data collection, preprocessing, and model training, enabling accurate and scalable irrigation management. We evaluate three models: a lightweight XGBoost regressor for edge deployment, a Long Short-Term Memory (LSTM) network for capturing temporal patterns, and a hybrid LSTM–XGBoost model that combines the strengths of both. The hybrid model achieved the best performance with a Root Mean Squared Error (RMSE) of 0.01705 and a coefficient of determination (R2) of 0.95, outperforming the standalone XGBoost (RMSE = 0.0184, R2= 0.92) and LSTM (RMSE = 0.0704, R2= 0.86) models. Operational insights regarding system latency, data reliability, and field maintenance are also discussed, emphasizing the model’s robustness and practical deployment potential. The results underscore the viability of data-driven irrigation forecasting for improving agricultural sustainability and optimizing resource efficiency.