Due to the coronavirus, millions of people worldwide carry out their work, education, shopping, culture, and entertainment activities from their homes using the advantages of today's technology now. Apart from this, patient care and follow-up are carried out with the help of electronic equipment especially in the institutions where health services are provided. It is important for humanity to perform all these services by providing a reliable electricity supply. In this study, outlook of the energy in Turkey were examined. The current energy consumption and investments were examined. Then, the precautions by the government against the pandemic period according to the occurrence and spread of COVID-19 in the country are given in chronological order. The actual electricity consumption data were obtained daily across the country, after all these precautions. It was observed that electricity consumption decreased significantly, especially on restricted days. It is inevitable that energy consumption estimation should be made in the short term so that the energy sector is not adversely affected by this situation. In this study, a more accurate short-term consumption forecasting methods were developed during the COVID-19 pandemic period, non-linear autoregressive (NARX) and long short term memory (LSTM) artificial neural networks. Between January-April 2019 electrical consumption data were used to train and validate the forecast prediction. The NARX and LSTM is a potential candidate for effective forecasting of electricity energy consumption. However, the obtained LSTM results suggest that the proposed method performed better results than NARX ANN.

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In this study, a buck converter and a driver has been designed for Linear Switched Reluctance Motor (LSRM) with 6/4 poles, 3 phases, 250W. LSRM controlled by PIC 18F452 microcontroller was used to drive automatic door. Motor was controlled more accurate with linear incremental encoder by sensing acceleration and movement direction of door.