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Load forecasting guides the power company to make some decisions on generation, transmission and distribution of electrical power. This work presents a solution methodology, using fuzzy logic approach for short term load forecasting (STLF) for Adamawa State University, Mubi. The proposed methodology utilized fuzzy reasoning decision rules that use the nonlinear relationships between inputs and outputs. The fuzzy logic model was developed in the Simulink environment of a MATLAB software. The model developed was able to forecast a day ahead load (kW) with a mean absolute error (MAPE) of 6.17% and it was observed that weather parameter (temperature) has significant impact on electrical load.

Because of its uniqueness and accuracy over time, fingerprint has been used for identification for many years, more recently being automated due to advancement in computing capabilities, fingerprint identification is one of the most well-known and popular biometric identification systems. The methodology comprised of a power supply, input voltage to the LM7805 should be at least 2v greater than the required 5v output according to its rating from the datasheet; hence it requires an input voltage of atleast 7v. Hence, 9v battery was adopted with current rating of 1A for convenience. The LM7805 voltage regulator IC was used since we needed 5v. The Fingerprints Attendance System with Data-login was designed, simulated and implemented/constructed, and was able to address the attendance issues in Adamawa State University, Mubi by the following means: no time waste as the attendance is taken during lecture without intervention of the lecturer, managing the attendance is automated, no chance for buddy signing, real time attendance capture, evaluate level of attendance for students automatically. Also, this system can easily be applied for examination attendance and monitoring.