The inherent variability in the power production of renewable energy sources (RES) limits the effectiveness of energy management systems (EMS) since optimal dispatch on power networks highly depends on the accuracy of predictors associated with the energy output and load demand. Consequently, power prediction tools for variable time horizons allow for improving energy management decisions. In this context, this work presents a detailed methodology for the deployment of predictive models for the photovoltaic (PV) power output of a small solar farm. The prediction models process a PV power dataset's time series using statistical techniques and neural networks with long-short term memory (LSTM). Before the data fitting, we develop a data preprocessing system, which involves evaluating missing data in the time series and getting descriptive analysis of the data set to either complete portions or delete atypical data. The results strongly suggest that the LSTM network performs better than the statistical model in exchange for more considerable computation times for long-term predictions.

The efficiency of a proportional-integral-derivative (PID) controller depends directly on the values of the gains$K_{p},K_{i}$, and$K_{\mathrm{d}}$. In this context, changes in the process dynamics imply degradation in the performance of the closed-loop control system. Several artificial intelligence techniques are used to adjust the PID parameters to improve the control system's performance. However, there is little exploratory evidence about how primitive techniques with low computational costs can contribute to solve this gain calculation task. This paper explores and compares three algorithms applied to the real-time gain calculation of PID controllers: fuzzy logic (FGS-PID), simulated annealing (SA-PID), and the A * search method (A *-PID). The validation of the operation of these schemes is carried out through simulations as well as by applying these controllers to a two-tank liquid-level control system. Experimental results show the algorithms' effectiveness to improve the control system's global performance and to add passive fault-tolerance capabilities.

Raw electromyography (EMG) signals are useful for several purposes in the analysis of muscles in both clinical and engineering applications. For instance, muscles fatigue is, typically, assessed by the mean frequency (MNF) of the EMG signal. Previous research works have shown that there is a positive correlation between the signal to noise ratio (SNR) of the EMG signal and its MNF value. The aim of this work is to determine and compare MNF in subjects with impairments in the lower limbs. Measurements of the MNF and the SNR were performed in nine muscles of six pathologic subjects, which were compared with a similar database of subjects without apparent pathologies. The MNF value was estimated using the power spectral density. The SNR for pathologic patients database was higher (17.28±1.67 dB) with respect to the database of subjects without apparent pathologies (12.86±1.71 dB). Subjects with pathologies in the lower limbs presented a decrease in the average value of the MNF.

This paper presents a methodology of design and implementation details of a solution based on Internet of things (IoT) applied to home automation. The solution is implemented using the system on a chip ESP8266. The proposed architecture allows the interaction between sensors and actuators (client nodes) linked through a home wireless network. The protocol was implemented in the application layer of the TCP/IP model, while subtle changes on the remaining layers of the TCP/IP stack are applied, as follows: 1) The IEEE 802.11 standard is used in the physical layer to define basic aspects such as transmitter power and receiver sensitivity; 2) CSMA/CA was used as a media access method, allowing multiple stations to use the same transmission medium; 3) in the network layer, the interaction between the nodes is configured in mesh mode, where each node uses a 32-bit identifier based on its MAC address; 4) the transport layer is configured using TCP, to guarantee reliability in data delivery; and, 5) the MQTT protocol is used in the application layer. To evaluate the performance of the proposed protocol, there were manufactured prototypes of the client nodes. An application for Android-based mobile devices was also developed, so that the user can control and manage the network from their own device.

This paper presents a methodology on the design of an optimal predictive control scheme applied to an islanded microgrid. The controller manages the batteries energy and performs a centralized load shedding strategy to balance the load and generation within the microgrid, and to keep the stability of the voltage magnitude. A nonlinear model predictive control (NMPC) algorithm is used for processing a data set composed of the batteries state of charge, the distributed energy resources (DERs) active power generation, and the forecasted load. The NMPC identifies upcoming active power unbalances and initiates automated load shedding over noncritical loads. The control strategy is tested in a medium voltage distribution system with DERs. This control strategy is assisted by a distribution monitoring system, which performs real-time monitoring of the active power generated by the DERs and the current load demand at each node of the microgrid. Significant performance improvement is achieved with the use of this control strategy over tested cases without its use. The balance between the power generated by the DERs and the load demand is maintained, while the voltage magnitude is kept within the maximum variation margin of ±5% recommended by the standard ANSI C84.1-1989.