https://estm.kgut.ac.ir/ Energy Systems: Technology and Management en daily 1 Sat, 21 Dec 2024 00:00:00 +0330 Sat, 21 Dec 2024 00:00:00 +0330 https://estm.kgut.ac.ir/article_232462.html The expansion of renewable energy sources in the residential sector, while an important step towards a more sustainable energy landscape, poses unique challenges for power system operators. The intermittent nature of renewable energy generation, primarily from solar and wind, introduces significant variability and uncertainty into the grid. Unlike conventional power plants, which provide steady and controllable output, renewable generation depends heavily on weather conditions, leading to rapid and sometimes unpredictable fluctuations in supply. To mitigate these challenges, system operators must invest in advanced grid management systems, flexible demand-response mechanisms, and large-scale energy storage solutions. Additionally, accurate forecasting of net load—defined as the total electricity demand minus renewable generation—is crucial for maintaining grid stability, optimizing energy dispatch, and reducing reliance on costly balancing reserves. Traditional forecasting methods often struggle to capture the nonlinear and stochastic nature of net load, particularly in systems with high renewable penetration. This paper presents a comprehensive framework for day-ahead net load forecasting, leveraging advanced machine learning techniques to improve prediction accuracy. The proposed VMD-MIIG-CNN-GRU-BiLSTM approach effectively captures both spatial and temporal dependencies in net load data while accounting for external factors such as weather patterns and consumption behavior. Experimental results demonstrate that the proposed model achieves a MAPE of approximately 8%, significantly outperforming conventional forecasting methods. The framework’s robustness in handling the inherent complexities of renewable-integrated power systems makes it a valuable tool for grid operators, enabling more efficient resource allocation and enhanced system reliability. https://estm.kgut.ac.ir/article_231015.html This study presents the development of a one-dimensional thermal model to analyze the performance of an improved inclined solar still equipped with a fan and phase change material (PCM). The modeling is based on mass and energy conservation equations applied to the various components of the system, incorporating all heat transfer mechanisms as well as the processes of evaporation and condensation. The proposed model is capable of estimating the temperatures of different parts of the solar still, as well as the hourly and daily fresh water production rates. Numerical simulation of the model was carried out in MATLAB using a time step of 0.1 seconds. In this research, the model was validated over a period of three days. The results demonstrate high accuracy of the thermal model, showing that it can reliably predict the component temperatures and freshwater yield under varying conditions. The model exhibited excellent precision in estimating the temperatures of the brine and absorber plate, with root mean square error (RMSE) values below 2.2 °C. For freshwater production, the maximum deviation between the model predictions and experimental data was less than 9% in the worst case. Parametric studies revealed that selecting an appropriate melting temperature for the PCM can enhance freshwater production by up to 5%, while the PCM mass had no significant effect on the still's performance. https://estm.kgut.ac.ir/article_232463.html Energy information systems are crucial for the operational optimization of smart buildings. These systems can offer benefits such as high energy-saving potential, efficiency, and intelligent services. Therefore, accurate electricity consumption forecasting requires a smart estimation strategy that considers the parameters affecting electricity consumption patterns. This paper proposes an intelligent prediction model that accurately forecasts and analyzes building electricity consumption. The proposed model includes the following components: a feature selection model based on mutual information for selecting input variables, and a deep learning time series prediction model based on Long Short-Term Memory (LSTM) neural networks to forecast the target value. The model's performance was evaluated using real-world data from a two-story smart home located in Houston, Texas, USA. A comparative analysis with other benchmark models was also conducted. The comprehensive comparison demonstrated that the hybrid model is more accurate than individual models, and the proposed intelligent model outperforms other benchmark hybrid and standalone models, as indicated by the achieved prediction performance. https://estm.kgut.ac.ir/article_231018.html The planning of energy systems with a high share of renewable energy sources has become increasingly important due to environmental concerns and energy security challenges. However, the large-scale integration of renewables into the grid requires effective strategies to ensure stability and reliability. One proposed approach to addressing these challenges is the diversification of renewable energy technologies, including both dispatchable and non-dispatchable sources, combined with various energy storage solutions, such as daily and seasonal storage. In this study, an open-source energy modeling tool, PyPSA, is used to optimize the operation and planning of a hybrid energy system. Using energy modeling in PyPSA, optimal strategies for deploying photovoltaic (PV) systems and battery storage are examined to achieve the island’s climate targets, which include 25%, 50%, and 100% CO₂ emission reductions by 2030, 2040, and 2050, respectively. The findings of this research provide valuable insights into cost-effective and sustainable pathways for integrating renewable energy into isolated energy systems, contributing to long-term energy transition strategies. https://estm.kgut.ac.ir/article_232461.html The water shortage crisis and its interconnected and integrated management are among the life crises of the current period. Factors such as population growth, diversity, and plurality of population needs, traditional farming methods, etc., help to increase the domain of this crisis. The complexity of water sources and the consumption system makes it hard to manage the process of its sources and decision-making about it. This research aims to study the effective factors in water sources and the consumption system of Rafsanjan city and make a simulated model of water shortage and its causes based on the system dynamics approach. In this research, in the beginning, the water sources and consumption system of the city were simulated. With the help of the statistical method of design of experiments, the involved variables in the water shortage in the city were tested, and also with the help of this method, the sensitive variables in the system were adjusted. Finally, scenarios based on the obtained amount of the above method were presented for coping with the water shortage in this city. https://estm.kgut.ac.ir/article_232464.html Abstract: Short-term load forecasting ( STLF ) is precisely required for planning, operation, and control of the power system. This forecasting method is used by electricity installations, system operators, generators, and energy marketers. In this paper, by applying the feature selection ( FS ) technique on the basis of the Mutual Information ( MI ) criterion, the most effective of these data are selected. The proposed method in this paper is to use the Composite neural network ( CNN ), which consisting of three feedforward neural networks of the type multi-layer perceptron that are connected in series. Also, the Particle swarm optimization method ( PSO ) is used to achieve optimal values of the impact parameters in the forecasting process, which are included in the feature selection section and neural network. this method has been applied to load data of year 97 cities of Kerman and its results emphasize the efficiency of this method in the forecasting of electric load.