This paper proposes a novel methodology for very short term forecasting of hourly global solar irradiance (GSI). The proposed methodology is based on meteorology data, especially for optimizing the operation of power generating electricity from photovoltaic (PV) energy. This methodology is a combination of k-nearest neighbor (k-NN) algorithm modelling and artificial neural network (ANN) model. The k-NN-ANN method is designed to forecast GSI for 60 min ahead based on meteorology data for the target PV station which position is surrounded by eight other adjacent PV stations. The novelty of this method is taking into account the meteorology data. A set of GSI measurement samples was available from the PV station in Taiwan which is used as test data. The first method implements k-NN as a preprocessing technique prior to ANN method. The error statistical indicators of k-NN-ANN model the mean absolute bias error (MABE) is 42 W/m 2 and the root-mean-square error (RMSE) is 242 W/m 2 . The models forecasts are then compared to measured data and simulation results indicate that the k-NN-ANN-based model presented in this research can calculate hourly GSI with satisfactory accuracy.
<span lang="EN-US">This paper presents the reptile search algorithm (RSA) method to optimize the proportional integral derivative (PID) parameters on direct current (DC) motors. RSA was adopted from crocodile hunting behavior. Crocodile behavior is modeled in two important steps: surrounding and attacking prey. The RSA method was applied using twenty-three classical test functions. The search method of the proposed RSA method with other existing algorithms such as particle swarm optimization (PSO), and differential evolution (DE). Integral multiplied by absolute error (ITAE) and integral of time multiplied squared error (ITSE) were used as comparisons in measuring the performance of the RSA method. The results show that the proposed method, namely RSA, has better efficiency. Optimization of PID parameters with RSA on DC motor control shows superior performance. From the experiment, the ITSE average value of the RSA method is 4.17% better than the conventional PID method.</span>
<p>The cooperation search algorithm (CSA) duplicates teamwork in every part of the company. This paper presents an approach to setting automatic voltage regulator (AVR) with proportional-integral-derivative (PID) control based on CSA. To get the performance of the proposed method, this paper uses the maximum overshoot, rise time, settling time, and error. This paper uses the whale optimization algorithm (WOA), grasshopper optimization algorithm (GOA), particle swarm optimization (PSO), and sine-cosine algorithm (SCA) methods as a comparison in measuring the performance of the CSA method which is used as the optimization of PID parameters on the AVR. From the simulation, the application of the CSA method to get the optimal PID parameter value has an average result that can reduce the maximum overshoot by 10.97% compared to the GOA, PSO and SCA methods.</p>
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