Dissertation (MSc Information System)Electricity cannot be stored efficiently in the grid, resulting in a need for demand and supply to be in balance. The Transmission System Operator operates a real-time electricity market to acquire extra supply or load. Traditionally Presently, the power demand is growing fast every day, which need more resources and different grid constructions. Tanzania is one of the developing countries which needs to acquire and make enough sources, must achieve load cracking to accomplish stability of the power system. This study aims to achieve accurate, real-time and interpretable forecasting of the electricity generation. This study identifies the best fit time series model for forecasting electricity generation in Tanzania. This underpins the development of a time series model for forecasting electricity generation. Several time series models including SARIMA, SVM and ANN were fitted to the data, and it emerged that the most adequate model for the data was ARIMA. The nonlinear relation of electric net power generation is explored by historical monthly recorded data, this relation can help Tanzania Electric Supply Company Limited to predict net electric generation for the next month. Experimental results show that our proposed electric generation forecasting method based on ARIMA can get a suitable prediction model and achieve high predicted precision, which is in accordance with the real data in the record. There will be no increases in electricity generation in the Mtera dam over the next 2 years. It is recommended that TANESCO should use the model and its forecasted figures in its operational and planning activities.