This article proposes a novel framework to improve the prediction accuracy of very short-term (5-min) wind power generation. The framework consists of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), monarch butterfly optimization (MBO) and long short-term memory (LSTM), called CEMOLS. The CEEMDAN is employed to extract complex hidden features of time-series data into intrinsic mode functions that are predicted using LSTM models with dropout regularization to retain long-term relationships between input and output data, while the optimization algorithm tunes the hyperparameters of the forecasting model. Data from four real wind farms in New South Wales are collected and preprocessed to train and test the forecasting models. Recently developed rival models are compared to identify the best-performing prediction model. The analysis demonstrates that the proposed CEMOLS with low computation time can improve forecasting accuracy on average by 32.96% in mean absolute error, 47.10% in root mean square error and 32.33% in mean absolute percentage error as compared to the benchmark Persistence model. It also demonstrates that sensitive and statistical analysis needs to be carried out to determine robust prediction models among rival models for practical application.

A novel dielectric embedded electronically steerable passive array radiator (ESPAR) (DE-ESPAR) antenna array for mobile wireless communication terminal is developed by finite element method based numerical modeling technique. The size reduction of the seven-element monopole antenna was accomplished by embedding the array in a cylindrical rod of FIK ceramic complex with a relative permittivity around /spl epsiv//sub r/=4.5. Dielectric embedded prototypes of a seven-element ESPAR antenna array were modeled and experimentally characterized. Experimental and numerical results are in good agreement. The optimized antenna produced a horizontal directivity of 5.1 dBi and return loss of 19 dB at 2.48 GHz. An overall volume reduction of 80% and footprint reduction of 50% were achieved.

This paper set out to identify the significant variables which affect residential low voltage (LV) network demand and develop next day total energy use (NDTEU) and next day peak demand (NDPD) forecast models for each phase. The models were developed using both autoregressive integrated moving average with exogenous variables (ARIMAX) and neural network (NN) techniques. The data used for this research was collected from a LV transformer serving 128 residential customers. It was observed that temperature accounted for half of the residential LV network demand. The inclusion of the double exponential smoothing algorithm, autoregressive terms, relative humidity and day of the week dummy variables increased model accuracy. In terms of R² and for each modelling technique and phase, NDTEU hindcast accuracy ranged from 0.77 to 0.87 and forecast accuracy ranged from 0.74 to 0.84. NDPD hindcast accuracy ranged from 0.68 to 0.74 and forecast accuracy ranged from 0.56 to 0.67. The NDTEU models were more accurate than the NDPD models due to the peak demand time series being more variable in nature. The NN models had slight accuracy gains over the ARIMAX models. A hybrid model was developed which combined the best traits of the ARIMAX and NN techniques, resulting in improved hindcast and forecast fits across the all three phases.