• Energy Research

This paper presents a decision support system for power load forecast and the learning of influence patterns of the socio-economic and climatic factors on the power consumption based on mathematical and computational intelligenge methods, with the purpose of defining the future power consumption of a given region, as well as to provide a mean for the analysis of correlations between the power consumption and these factors. Here we use a linear modelo of regression for the forecasting, also presenting a comparative analysis with neural networks, to prove its efectiveness; and also Bayesian networks for the learning of causal relationships from the data.

Optical current sensors have been developed and improved over the past few decades, and they have been increasingly employed in power systems, including smart and high-voltage grids. This is due to their many advantages over conventional electromagnetic current sensors, such as reduced size and weight, greater operational safety, and electromagnetic immunity. Like any measuring instrument or system, their quality and reliability are associated with measurement uncertainty, which quantifies their precision. This measurement uncertainty depends on a series of influencing quantities, such as the wavelength of light used in the sensor, the birefringence of the optical material used in the construction of the sensor, and environmental conditions, such as temperature and vibration. This article presents a review of the main influences that affect the quality and performance of optical current sensors and how these influences can be used to estimate measurement uncertainty. The main objective is to serve as a guide or reference for the identification and evaluation of uncertainty sources in optical current sensors used in power systems.

Abstract One of the most desired aspects for power suppliers is the acquisition/sale of energy for a future demand. However, power consumption forecast is characterized not only by the variables of the power system itself, but also related to social–economic and climatic factors. Hence, it is imperative for the power suppliers to project and correlate these parameters. This paper presents a study of power load forecast for power suppliers, considering the applicability of wavelets, time series analysis methods and artificial neural networks, for both mid and long term forecasts. Both the periods of forecast are of major importance for power suppliers to define the future power consumption of a given region. The paper also studies the establishment of correlations among the variables using Bayesian networks. The results obtained are much more effective when compared to those projected by the power suppliers based on specialist information. The research discussed here is implemented on a decision support system, contributing to the decision making for acquisition/sale of energy at a future demand; also providing them with new ways for inference and analyses with the correlation model presented here.

This paper investigates the performance of scalable user-centric (UC) distributed massive multiple-input multiple-output (D-mMIMO) systems with multiple central processing units (CPUs), commonly called cell-free mMIMO. Specifically, a framework incorporating processing capacity and inter-CPU communication constraints is proposed. Two methods are presented for limiting the number of radio units (RUs) serving each user equipment (UE). The first method is performed by the CPUs, while the second one is implemented at the UEs and RUs. Both methods prevent the computational complexity (CC) for channel estimation and precoding signals from increasing with the number of RUs. The backhaul signaling demands are presented and modeled, and it is considered that each CPU can serve only a restricted number of UEs managed by other CPUs to mitigate inter-CPU communication. Two strategies to adjust the RU clusters according to the network implementations are also proposed. We compare the proposed approaches with a traditional scalable UC system. Simulation results reveal that the proposed techniques allow UC systems to keep their spectral efficiency (SE) under minor degradation while reducing the CC by 98% and improving energy efficiency (EE). Besides, managing inter-CPU communication controls backhaul traffic effectively, and RU cluster adjustments further reduce CC. Post-print / Final draft

One of the most desired aspects for power suppliers is the acquisition/sell of energy in a future time. This paper presents a study of load forecasting for power suppliers, presenting a comparative application of the techniques of wavelets, time series methods and neural networks, considering short and long term forecast; both of great importance for power suppliers in order to define the future power consumption of a given region.