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Due to the spread of COVID-19 across the world and the increased need for non-contact thermometers to prevent the spread of disease, a new electronic thermometer has been designed and implemented for measuring human body temperature from a distance. This device is currently in use at building entrances to measure the body temperatures of employees, students, and customers. This system is designed using low-cost easy-to-assemble open-source electronic components. The system consists of seven main parts: an Arduino UNO microcontroller, an infrared (IR) thermometer for non-contact temperature measurements (GY-906 MLX90614ESF module), an IR motion sensor (TCRT 5000) for the purpose of contactless initiation of the system, a graphic LCD to display results, a DS3231 clock module for a real-time clock and calendar, and a micro-SD storage board to store device audio instructions.

Electrical distribution system loads are permanently not fixed and alter in value and nature with time. Therefore, accurate consumer load data and models are required for performing system planning, system operation, and analysis studies. Moreover, realistic consumer load data are vital for load management, services, and billing purposes. In this work, a realistic aggregate electric load model is developed and proposed for a sample operative substation in Baghdad distribution network. The model involves aggregation of hundreds of thousands of individual components devices such as motors, appliances, and lighting fixtures. Sana’a substation in Al-kadhimiya area supplies mainly residential grade loads. Measurement-based techniques are to be used in estimating the substation load model parameters. The proposed model accounts for the effect of the feeders, the LV- transformers and the compensation devices present in the system. The model validation is evident from calculated results comparison to realistic measured data.

Generation unit’s commitment is a large complex decision making problem due to many difficulties such as multiple constraints, variety of unit’s specification and different fuel used. In this work a hybrid intelligent technique is produced by genetic algorithm seeded by LaGrange multiplier method to solve the unit commitment problem for a 10 units large dimension network system using multiple mitigation techniques. This is to reduce the adverse impacts of the difficulties mentioned above. The solution results in an optimal operation schedule during a 24 hours load profile serving a load demand in addition to a required spinning reserve