Browsing by Author "Dias, Paloma Greiciana de Souza"
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- Development of surplus power generation forecast for use by residential loadsPublication . Dias, Paloma Greiciana de Souza; Brito, Thadeu; Silva, William R.; Pereira, Ana I.; Lopes, Luis C.G.; Santos, Murillo F. dos; Costa, Paulo Gomes da; Lima, JoséEnergy consumption has been increasing in the last years and thus, energy efficiency is one of the most important topics actually. Besides, the consumption and energy generation forecast help in efficiency optimization. This paper presents the development of a system for forecasting surplus power generation to be used by residential loads connected to smart plugs. In this way, it is intended to collaborate with the use of surplus energy production in electrical devices in a residence instead of sending to batteries or to the grid. This work presents the theoretical basis of the project and the architecture of the developed system. A Machine Learning method applied to photovoltaic generation data in a residence was used to predict surplus energy.
- Development of surplus power generation forecast for use by residential loadsPublication . Dias, Paloma Greiciana de Souza; Brito, Thadeu; Silva, William Rodrigues; Pereira, Ana I.; Lopes, Luis C.G.; Santos, Murillo F. dos; Costa, Paulo Gomes da; Lima, JoséEnergy consumption has been increasing in the last years and thus, energy efficiency is one of the most important topics actually. Besides, the consumption and energy generation forecast help in efficiency optimization. This paper presents the development of a system for forecasting surplus power generation to be used by residential loads connected to smart plugs. In this way, it is intended to collaborate with the use of surplus energy production in electrical devices in a residence instead of sending to batteries or to the grid. This work presents the theoretical basis of the project and the architecture of the developed system. A Machine Learning method applied to photovoltaic generation data in a residence was used to predict surplus energy.
- Smart system for monitoring and controlling energy consumption and ambient conditionsPublication . Dias, Paloma Greiciana de Souza; Brito, Thadeu; Lopes, Luis C.G.; Lima, JoséIn the current energy context, alternatives are sought that provide a more conscious use of energy and the development of technology aimed at efficiently meeting the needs of energy consumers and the utility company. In this scenario, smart systems for monitoring and controlling the energy consumption of residential loads stand out. In [1], the authors worked on a system from which the user could monitor their energy consumption in real time. Through a website, the consumer accessed their information using visualizations in graphics, for example. Consumption data was obtained by a smart plug. Furthermore, the option to remotely turn devices on and off has been included in the system so that the user has the ease of controlling their devices.
- Smart system for monitoring and controlling energy consumption by residence production and loadPublication . Dias, Paloma Greiciana de Souza; Brito, Thadeu; Lopes, Luis C.G.; Lima, JoséMonitoring and controlling the energy consumption of electrical appliances brings significant benefits to both consumers and the energy utility. This work presents a system for monitoring and controlling energy consumption by residence loads connected to smart plugs. The user will have a tool to view consumption information and remotely turn loads on and off, as well as control the power level at which certain appliances will operate. In addition, it is intended to give the system the ability to make decisions regarding the operation of electrical devices based on the electrical energy available. This decision-making can occur either through priorities established by the user or, possibly, through Machine Learning applied to the system, based on the consumption pattern. Solutions like these can even be applied in situations where the user produces his own energy and would like to use the surplus produced to meet certain loads.
- Smart system for monitoring and controlling energy consumption by residential loadsPublication . Dias, Paloma Greiciana de Souza; Lima, José; Lopes, Luis C.G.; Brito, ThadeuThis work proposes the development of a smart system for monitoring and controlling surplus energy consumption by residential loads connected to smart plugs. Data processing, storage and monitoring tools will be used, specifically Node-RED, InfluxDB, Grafana and Home Assistant. The latter allows remote control of devices through its interface. Through these tools, the user can visualize excess power data, which, in this work, are obtained through a hardware prototype that uses the ESP32 as a microcontroller and is responsible for measuring voltage and current of energy to analyze negative power and positive or surplus. Based on this measurement, load control tests were carried out based on the ratio of available energy to the power required by the load, using the Home Assistant interface. Then, this work focused on performing the excess power prediction by the linear regression method, a Machine Learning approach. A dataset with values of consumption and generation of photovoltaic energy in a residence was used and forecast analyzes were performed. The predicted results meet the expected objectives, although it is reasonable to conclude that further studies can still be done to ensure the robustness of the prediction model.