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.

A Smart-Grid (SG) represents an advanced electrical network designed for intelligent and efficient management across its entire infrastructure, facilitating seamless communication and coordination among its interconnected components, including IoT-enabled devices, and leveraging real-time data transfer mechanisms. This paper focuses on examining the local level within the SG, primarily tasked with monitoring energy consumption. To gain a comprehensive understanding of these concepts and the strategies employed for effective energy management, an energy management system has been devised. The primary objective of this system is twofold: first, to minimize the overall energy consumption within the SG, ensuring it remains within or below the received energy quantity; and second, to optimize the utility of appliances while ensuring they do not surpass the total energy capacity supplied by the Photovoltaic Panels (PVPs). To achieve this, we employ the Knapsack algorithm, wherein the locally produced energy serves as the algorithm's capacity, and the devices seeking energy consumption are treated as objects. Each device's weight, fixed consumption, and utility values are considered as attributes defining these objects, aiding in the algorithm's decision-making process. Through this approach, we aim to develop a robust energy management framework capable of efficiently allocating resources while maximizing overall utility within the SG ecosystem.

Artificial intelligence methods have aided the advancement of several disciplines of science and technology. Furthermore, they have had a significant influence on smart grid management. One of the most significant information for optimal management in Smart Grid is the ability to predict its parameters: electricity consumption and meteorological factors. It is mostly utilized to develop improved control ways for building appliances (such as lighting and heating/cooling systems). Several methodologies for load and weather data characterization prediction have recently been proposed. The work discussed in this paper is aimed at the development and the comparison of three artificial intelligence forecasting approaches used to manage the Smart Grid by integrating load and climate data predictions. To achieve this objective, we primarily looked on the predicting accuracy of some artificial intelligence methodologies: a neural network, a neuro-fuzzy and a deep learning prediction algorithms are applied and compared to forecast the smart grid parameters (temperature, solar radiation, wind speed and the energy consumption). The simulation results are checked based on real database of wind speed, temperature, solar radiation, and consumption data. The findings of the simulation give us an idea about the most appropriate and performant algorithm to use in this aim.

This paper deals with the problem of the optimal sizing in the PVP/Battery system. To achieve this aim, an ANFIS estimation algorithm has been developed in order to estimate a data base of instantaneous photovoltaic power. The estimated instantaneous PV power has been used in an optimal algorithm for sizing a PVP / Battery power station to supply a 1.5 Kw AC load.The simulation of the proposal sizing system has been implemented in Matlab. The results of the simulation give a good performance of our proposal sizing system.