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In this study, a new analytical model is developed for an electrostatic Microelectromechanical System (MEMS) cantilever actuator to establish a relation between the displacement of its tip and the applied voltage. The proposed model defines the micro-cantilever as a rigid beam supported by a hinge at the fixed-end with a spring point force balancing the structure. The approach of the model is based on calculation of the electrostatic pressure centroid on the cantilever beam to localize the equivalent electrostatic point load. Principle outcome of the model is just one formula valid for all displacements ranging from the initial to the pull-in limit position. Our model also shows that the pull-in limit position of a cantilever is approximately 44% of the initial gap. This result agrees well with both simulation results and experimental measurements reported previously. The formula has been validated by comparing the results with former empirical studies. For displacements close to the pull-in limit, the percentage errors of the formula are within 1% when compared with real measurements carried out by previous studies. The formula also gives close results (less than 4%) when compared to simulation outcomes obtained by finite element analysis. In addition, the proposed formula measures up to numerical solutions obtained from several distributed models which demand recursive solutions in structural and electrostatic domains.

Precise electricity demand forecasting has principal significance in the energy production planning of the developing countries. Especially during the last decade, numerous recent methods have been utilized to predict the forthcoming electricity demand in different time resolutions accurately. This contribution presents a novel approach, which improves the forecasting of Turkey’s electricity demand in monthly time resolution. An artificial neural network model has been proposed with appropriate input features. Yearly-based gross demand shows approximately linear increment due to population increase and economic growth, while monthly-based gross demand indicates an oscillation due to the effect of seasonal temperature fluctuations. However, there is no clear linear relation between electricity demand and temperature; for the ideal case, it is the V-shaped curve around balance point temperature. Since temperature levels in each region of the country demonstrate a high variance even in the same time period, weighted average temperature point was calculated with respect to the population weights of the selected regions of Turkey. In order to fit a function for monthly oscillations, a linear function according to weighted average temperature point was created. Unemployment data was added to the training data set as an indicator of economic fluctuations. The mean absolute percentage errors of the model were calculated for training, validation, and testing as 3.77 %, 2.02 %, and 1.95 % respectively.

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