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.

Abstract
Purpose This paper investigates the effect of the fit between supply and demand uncertainty (SDU) and supply chain responsiveness (SCR) (SC fit) on business and innovation performance in Brazilian companies. Design/methodology/approach The study presented an analysis carried out on an empirical study based on a sample of 150 manufacturing companies. Business and innovation performance of companies with different types of SC fit ( high-high and low-low fits) and misfit (positive and negative) are compared and discussed. Findings The results indicated that SC fit had a positive effect on both business and innovation performance. Further analyses suggested that companies with SC fit present similar business performance, independent of the level of SDU that characterizes the environment where they compete, while companies in environments with higher levels of uncertainty tend to present superior innovation performance. Companies with positive and negative misfit present similar performance. Originality/value An analysis of the literature showed that there is no consensus when it comes to the definitions and measurements of SC fit. The paper investigates the effects of SC fit on business and innovation performance, while previous empirical studies have mainly addressed its impact on financial performance. Moreover, this study compares the effects of two types of fit and two types of misfit and assesses SC fit in Brazilian manufacturing companies, analyzing the context of an under-researched reality.

Abstract
This article proposes a new two-stage hybrid stochastic-information gap-decision theory (IGDT) based on the network-constrained unit commitment framework. The model is applied for the market clearing of joint energy and flexible ramping reserve in integrated heat- and power-based energy systems. The uncertainties of load demands and wind power generation are studied using the Monte Carlo simulation method and IGDT, respectively. The proposed model considers both risk-averse and risk-seeker strategies, which enables the independent system operator to provide flexible decisions in meeting system uncertainties in real-time dispatch. Moreover, the effect of feasible operating regions of the combined heat and power (CHP) plants on energy and flexible ramping reserve market and operation cost of the system is investigated. The proposed model is implemented on a test system to verify the effectiveness of the introduced two-stage hybrid framework. The analysis of the obtained results demonstrates that the variation of heat demand is effective on power and flexible ramping reserve supplied by CHP units.