Analysis of Long Term Electricity Load Forecasting in Garhwal and Kumaon Zone of Uttarakhand using Artificial Neural Network

India is a developing nation where lots of activities are going on in the area of development. Uttarakhand is 27
^th
State of India which was formed on $9^{\mathrm{th}}$ November, 2000. The 90% of area is fall under hilly region and only 10% of area is comes under plain. The Uttarakhand State is rich in hydropower with total potential of 24, 551 Mega Watt (MW) where 15% of its total hydro potential is generated from hydropower. The Uttarakhand State has two main regions Garhwal and Kumaon Regions. The most of Hydro Power Projects are fall under Garhwal region. The demand of electricity is very much essential nowdays. India is going to become mega renewal energy producer by the year 2047. The per day demand of electricity in Uttarakhand is near by 35 Million Units (MU) however only 20 MU is generated from state hydro power stations. At present the situation is that the 3806.35 MW power is generated in Uttarakhand State. There are three main power utilities in Uttarakhand namely Uttarakhand Jal Vidyut Nigam Limited (UJVNL), Power Transmission Corporation of Uttarakhand Limited (PTCUL) and Uttarakhand Power Corporation Limited (UPCL). There is one Power Regulator in State namely Uttarakhand Electricity Regulatory Commission (UERC) established by Central Electricity Regulatory Commission (CERC) which governs the laws and orders related to power segment of the state. The Solar and Gas based power plants are also fulfilling the demand of consumers in the state. Due to the AT & C losses there is huge deficit in demand side management. In this study the power demand of Garhwal and Kumaon zone of Uttarakhand is analysed for 10 years from year 2022 to 2032. There are various techniques for power forecasting however in this study Artificial Neural Network method is used to forecast the demand of Garhwal and Kumaon Regions of Uttarakhand from year 2022 to 2032. The GDP, Population and Historical load are taken as input parameters to perform the results. The results are compared with UPCL forecasted demand.