A new generation of smart meters are called cognitive meters, which are essentially based on Artificial Intelligence (AI) and load disaggregation methods for Non-Intrusive Load Monitoring (NILM). Thus, modern NILM may recognize appliances connected to the grid during certain periods, while providing much more information than the traditional monthly consumption. Therefore, this article presents a new load disaggregation methodology with microscopic characteristics collected from current and voltage waveforms. Initially, the novel NILM algorithm—called the Power Signature Blob (PSB)—makes use of a state machine to detect when the appliance has been turned on or off. Then, machine learning is used to identify the appliance, for which attributes are extracted from the Conservative Power Theory (CPT), a contemporary power theory that enables comprehensive load modeling. Finally, considering simulation and experimental results, this paper shows that the new method is able to achieve 95% accuracy considering the applied data set.
Novel intelligent systems to assist the energy transition and improve sustainability and people's life can be deployed at different scales, ranging from a house to an entire region. University campuses are an interesting intermediate size (big enough to matter and small enough to be tractable) for research, development, test and training on the integration of smartness at all levels, which led to the emergence of the concept of "smart campus" over the last few years. This review article proposes an extensive analysis of the scientific literature on smart campuses from the last decade (2010-2020). The 182 selected publications are distributed into seven categories of smartness: smart building, smart environment, smart mobility, smart living, smart people, smart governance and smart data. The main open questions and challenges regarding smart campuses are presented at the end of the review and deal with sustainability and energy transition, acceptability and ethics, learning models, open data policies and interoperability. The present work was carried out within the framework of the Energy Network of the Regional Leaders Summit (RLS-Energy) as part of its multilateral research efforts on smart regions.
Based on the development of new technologies in the electrical engineering field, microgrids can be understood as the effective implantation of smart grids. These, in turn, have functionalities for energy management, such as voltage control, frequency, and demand management, and can also operate in a connected or island mode concerning the utilities resources. In the face of such technological advances and energy management, this paper presents a proposal for the implementation of a microgrid, called CampusGRID. This microgrid will be installed on the University Campus of UNICAMP - Brazil, being connected to a 11.9kV level voltage grid with 2370 kVA power rated shared among eleven points of connections and demand varying from 475 to 768 kW. For the control of loads (electric vehicle, classroom buildings, laboratories, libraries, convention center, multidisciplinary gymnasium), it is proposed to automate the circuits in the secondary side of transformers to control the loads, as well as the monitoring of these. For the power generation system, a set of distributed energy resources (DER) was considered, such as photovoltaic sources (PV), sources with cogeneration known for the combination of heat and power (CHP) using natural gas and storage system with lithium-ion batteries. These energy resources will be controlled by a centralized energy management system, with fiber-optic network communication, ensuring signal synchronism to the equipment for the provision of services, as well as serving as a means to collect the data set from the respective equipment for studies and performance improvements of the CampusGRID microgrid. Keywords: Microgrid; DER; EMS; Smart Grid;
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