Fractal dimension is mathematically defined as a ratio of statistical complexity of network traffic; its significant manifestation can affect the network performance. In this work, two models of corporate computer networks have been developed using optimized network engineering tool (OPNET) technology. Raw packet generator (RPG) traffic was imposed on the corporate networks and modeled using H = 0.7 and D = 1.3, under the influence of Pareto distribution. Autocorrelation function and power law were used to confirm the presence of fractal traffic on the networks. Average Hurst index (H) of 50 and 100 workstations were estimated using aggregate of variance, absolute moment, periodogram and R/S methods as 0.627, 0.608 and its corresponding fractal dimensions (D) were obtained as 1.371 and 1.391 respectively. These results obtained mean, there is a manifestation of fractal traffic and delay is minimised on the network.  

Electrical load forecasting is very important for effective planning and management of power system. Accurate load forecasting helps the electrical power company to make some decisions on how to meet up with their consumers’ demand.  This paper presents a solution methodology using fuzzy logic approach for short term load forecasting (SLTF) for Adamawa State University. The proposed method used fuzzy reasoning decision rules that utilized the nonlinear relationship between inputs and output. The model developed was able to forecast the future load with mean absolute percentage error (MAPE) of 1.36% and forecasting without previous load at the input of the fuzzy logic model yields better prediction error. However, from the result obtained, it shows temperature has a significant impact on electrical load than the relative humidity. Also, electrical load shall increase by 1.84 kW on the 25th September, 2018 due to increase in temperature.