DC energy solutions are gaining interest in recent years, due to improvements in power electronics. Some companies have developed solutions for the domestic implementation of DC power systems. This article analyzes the state of the art about domestic DC applications and compares the efficiency of these systems in comparison to conventional AC systems. Different loads of a building as lighting, elevator or heating and renewable energies as PV panels or small wind generator are modeled for both AC and DC connections. With these models the power consumption of the building is simulated to compare the performance of both systems.

The massive integration of distributed generation (DG) can cause voltage disturbances at the connection points. In order to maximize the penetration of DG power, the actual topology of distribution grids should change introducing active devices to control the voltage, especially in weak networks. In this paper it is analyzed the integration of DG in a weak network connected to a MV/LV substation, using different active devices connected to this substation. The studied active devices are: OLTCs, STATCOM with energy storage and dump-loads. The contribution of these active devices to increase the installed DG power in weak networks is analyzed. An operation and control strategy for the active substation is presented and the performance of this active substation is verified by temporal power flow simulations.

Weak electrical grids like islands or other remote power systems are more sensitive than larger systems to frequency deviations. Due to the increasing renewable energy penetration in islands and other weak electricity networks, this sensitivity has increased since the renewable energy sources are deploying conventional generators. In this context it is necessary to assure primary power reserve to respond in front of important power disturbances. In this article it is analyzed the use of Ultracaps to respond in the face of the disconnection of a generation unit in a weak island grid, where this disturbance can lead to a general collapse of the power system. It is shown that Ultracaps can be a suitable solution to improve the primary power response of a power system.

Power consumption prediction is a crucial component in enhancing the efficiency and sustainability of building operations. This study investigates the impact of data collection frequency and model selection on the predictive accuracy of power consumption in two distinct building types: an Academic one with 15-min interval data and an Industrial one with hourly data. Various machine learning models, including Support Vector Machine (SVM) with Radial and Sigmoid kernels, Random Forest (RF), and Deep Neural Networks (DNNs), across different data splits and feature sets, were considered. Our analysis reveals that higher data collection frequency generally improves model performance, as indicated by lower RMSE, MAPE, and CV values, alongside higher R² scores. The inclusion of more historical power consumption features was also found to have a more significant impact on the accuracy of predictions than including climate condition features. Moreover, the SVM-Radial model consistently outperformed others, particularly in capturing complex, non-linear patterns in the data. However, the DNN model, while competent in some metrics, showed elevated MAPE values, suggesting potential overfitting issues. These findings suggest that careful consideration of data frequency, features, and model selection is essential for optimizing power prediction, contributing to more efficient power management strategies in building operations.