Abstract
Agriculture plays a significant role in the labor force and GDP of Mozambique. Nonetheless, the energy source massively used for water pumping in irrigation purposes is based on fossil fuels (diesel oil). Despite the water availability and fertile soils in Moamba, Mozambique, farmers struggle with the high cost of fuels used in the pumping systems. This study was sought to analyze the feasibility of utilizing a solar photovoltaic system as a means to reduce the environmental impact caused by the diesel pumps and simultaneously alleviate the expenses regarding the use of non-environmentally friendly technologies. Site observations and interviews were undertaken in order to obtain local data regarding the water demand, current energy systems costs and distances from the source to the irrigated fields. CLIMWAT 2.0 was used for climate data acquisition and analysis. The environmental benefits, the cost effectiveness and local climate conditions show that the PV system is feasible in Moamba. Furthermore, parameters such as hydraulic energy, incident solar energy, pump efficiency and total system efficiency were used to predict the performance of the system. The results obtained are important to analyze the implementation of such energy systems.

Abstract
The management of urban solid waste represents a great challenge to humanity. The current scenario of pollution due to waste that is still being incorrectly disposed of has brought us to an alarming situation. To progress and overcome the barriers, the sector needs changes and innovations. Waste management is not only the responsibility of municipalities; it must also involve people. This chapter presents a technological solution that fosters people's involvement in waste management practices. Through the use of this platform, users can register the waste produced and evaluate their performance in recycling management according to several types of residues considering the targets set by the municipalities. This approach may be relevant for the implementation of pay-as-you-throw models in municipalities. © 2021, IGI Global.

Abstract
The energy efficiency requirements of most energy-consuming sectors have increased recently in response to climate change. For buildings, this means targeting both facility managers and building users with the aim of identifying potential energy savings and encouraging more energy-responsible behaviors. The Information and Communication Technology (ICT) platform developed in Horizon 2020 FEEdBACk project intends to fulfill these goals by enabling the optimization of energy consumption, generation, and storage and control of flexible devices without compromising comfort levels and indoor air quality parameters. This work aims to demonstrate the real-world implementation and functionality of the ICT platform composed of Load Disaggregation, Net Load Forecast, Occupancy Forecast, Automation Manager, and Behavior Predictor applications. Particularly, the results obtained by individual applications during the test phase are presented alongside the specific metrics used to evaluate their performance.

Abstract
Electrical utilities apply condition monitoring on power transformers (PTs) to prevent unplanned outages and detect incipient faults. This monitoring is often done using dissolved gas analysis (DGA) coupled with engineering methods to interpret the data, however the obtained results lack accuracy and reproducibility. In order to improve accuracy, various advanced analytical methods have been proposed in the literature. Nonetheless, these methods are often hard to interpret by the decision-maker and require a substantial amount of failure records to be trained. In the context of the PTs, failure data quality is recurrently questionable, and failure records are scarce when compared to nonfailure records. This work tackles these challenges by proposing a novel unsupervised methodology for diagnosing PT condition. Differently from the supervised approaches in the literature, our method does not require the labeling of DGA records and incorporates a visual representation of the results in a 2D scatter plot to assist in interpretation. A modified clustering technique is used to classify the condition of different PTs using historical DGA data. Finally, well-known engineering methods are applied to interpret each of the obtained clusters. The approach was validated using data from two different real-world data sets provided by a generation company and a distribution system operator. The results highlight the advantages of the proposed approach and outperformed engineering methods (from IEC and IEEE standards) and companies legacy method. The approach was also validated on the public IEC TC10 database, showing the capability to achieve comparable accuracy with supervised learning methods from the literature. As a result of the methodology performance, both companies are currently using it in their daily DGA diagnosis.

Abstract

Abstract
The detection and extraction of individual pylons and power lines from high-density point cloud (PC) LiDAR data are a relevant tool for evaluating the power lines utility corridors. Moreover, the presence of high vegetation and hilly terrain is a research challenger in the available methods. The paper presents a novel method for the extraction of pylons and power lines. Two steps compose the proposed approach: a pylon detection step based on top view projection, denoted by DFSS - Detect Filled Square Shapes, and a pylon arms detection step with the DPA Detect Pylon Arm algorithm. The results show that the proposed method could accurately and automatically extract pylons and the associated power lines, even if the dataset has low quality with downsampling, to reduce the processing time. Field tests were performed with a ground static LiDAR and a point cloud affected by downsampling voxel grid and Gaussian noise to simulate the expected LiDAR data from a UAV.