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Seasonal Variation of Power Distribution in Niger State of Nigeria using Markov Model with Non-Stationary Transition Probabilities

This paper presents the application of Markov chain model with non-stationary transition probabilities to study the monthly data of the power distribution in Niger state in the wet, Dry-Hot and Hamatten/Dry- Hot seasons. The result indicates an optimal power distribution of over 150,000MWwith probability 0.49 during the wet season, 0.25 during the hot-dry season and 0.19 in the hot-cold season respectively. The variation of power distribution directly affects the electricity consumers. Markov chain model could be used as a predictive tool for determining the power distribution pattern at different seasons in the Study area. These predictions might be used for the management of (NCC) for effective distribution of megawatts. Keywords : Markov Chain, Transition probability, Non-stationary, Power Distribution