IMPROVING THE DISTRIBUTION SYSTEM LOAD FORECASTING OF URBAN CONGESTION AREAS USING ARTIFICIAL INTELLIGENCE

Power system is paramount important to drive development and industrialization. Every day, both residential and commercial areas yearn for improved power to drive their day-to-day running of their business. In already congested areas, many buildings are demolished and towers are erected. Simple industrial machines are phased out and replaced with heavy industrial machines. The increase in load exceeds the capacity of the distribution transformer and the distribution substation capacity. These problems cannot be handled by short-term, midterm, or long-term load forecasting techniques only, due to innovation or fast development in already urban, congested areas. These uncontrolled practices led to the burning of power transformers, distribution transformers, incessant faults, and unwanted outages and frequent load shedding. These affected many businesses and caused psychological trauma to residential customers. A hybrid technique embedded with artificial intelligence was modeled to mitigate the incessant load shedding, especially during the peak period, and reduce burning of power facility. The hybrid technique was modeled and infused into First Power Distribution network which interface with the indoor panel. The technique predict futuristic load and characterize the load before introduction into the network. When the load introduction exceeds the carrying capacity, it will trigger the circuit breaker to trip automatically. Before the hybrid technique embedded with artificial intelligence, the outage rate is 50 percent but with the technique embedded with artificial intelligence. The force outage rate was reduced to 15 percent.