As photovoltaic (PV) systems have been successfully adopted worldwide, accurate power generation forecasting becomes increasingly essential to stable power grid operation and smart grid applications to cope with the variability of PV systems. Several data-driven models have recently been proposed for the more accurate prediction of PV power generation and have shown good performance. In particular, hybrid models that combine the characteristics of single-structure deep learning-based models have achieved better accuracies. To this end, a novel ultra-short term PV power generation forecasting model with a hybrid structure is proposed for instantaneous response to PV fluctuations. For higher forecasting accuracy, the proposed model decomposes the input feature data into trend and residual components and employs customized sub-models such as the linear, Transformer, and long short-term memory (LSTM). Furthermore, the proposed model is trained with data from the self-built PV site to implement a model suitable to real-world applications. Finally, the experimental results demonstrate that the proposed model has the best forecasting performance compared to conventional and state-of-the-art deep learning-based forecasting models with reasonable computational complexity.

In wireless power transfer (WPT) systems, the electromagnetic fields generated by a charging module may exceed the limits set by international safety guidelines. This is a matter of concern for the safety of users of high-power WPT systems, such as electric vehicles (EVs). To address this issue, this study designed a stationary WPT system for EV charging. Furthermore, the dosimetry of the system was evaluated for two exposure scenarios. Electromagnetic field data obtained using the electromagnetic field analysis tool were employed to derive the induced quantities in the human body using the impedance method. In addition, the obtained results were compared to the values recommended by international guidelines (International Commission on Non-Ionizing Radiation Protection).

In the paper, LDPC (low-density parity-check) coded OFDM system applicable for impulsive noise channel, such as powerline channel (PLC) will be presented. Performance degradation in the proposed system over impulsive noise channel is mitigated using a simple estimator based on the Gaussian approximation with estimated channel information (ECI) and a signal level limiter. At first, it will be shown that the performance of a conventional LDPC coded OFDM system is seriously degraded due to the imperfection of estimating the initial LLRs (log likelihood ratios) of sum-product decoder over impulsive noise channels and hard evaluation of the initial LLRs from the decentralized noise samples by OFDM demodulator. Thereafter, to overcome the problems, the system using the simple estimator and the received signal level limiter is proposed. Simulation results show that the proposed system has much better performance than that of conventional systems. For the verification, Monte Carlo simulation is applied

With the growing global emphasis on environmental protection policies, renewable energy generation systems have become widely adopted. In particular, photovoltaic (PV) systems have gained popularity for their ease of management, while digital twin (DT) systems are being developed to enable real-time monitoring and management of the systems. Furthermore, the DT systems simulate the operations of the physical systems in real-time based on the data collected from various sensors. To this end, a novel sensor data generation model based on numerical weather prediction (NWP) data is proposed to forecast the future operations of PV systems using DT systems. The proposed model utilizes a hybrid data-driven model structure combining supervised learning-based long short-term memory (LSTM) and unsupervised learning-based generative adversarial network (GAN) to enhance both average and variance accuracy. Specifically, TransTimeGAN is proposed, which combines TimeGAN with Transformer to effectively capture 15-min variability. For practical applicability, the proposed model is trained and validated using data from a self-developed PV DT system. To evaluate the effectiveness of the proposed model, the similarities between normalized real and generated data are compared using a range of error metrics and statistical metrics. For representative error metrics, the proposed model achieves a mean squared error (MSE) of 7.84e-3 and a dynamic time warping (DTW) score of 1.3769. Regarding representative statistical metrics, the model achieves a Kullback-Leibler divergence (KLD, max-normalized) of 0.9591 and a standard deviation similarity (SDS) of 0.9671. The experimental results demonstrate that the proposed model delivers superior performance in generating data compared with various data-driven models across a range of numerical metrics and visual assessments.

In recent years, the channel measurement method using PN sequence have been suggested as the best choice for power-line communication (PLC) channel with respect not only that it can give the complex impulse response which can be converted to the frequency response but also that it can be effectively adopted even over noisy and long-length power line channel. However, finding each multipath from the measured impulse response is still challengeable. In this paper, we introduce the noble technique systematically finding each multipath from the measured complex impulse response using matrix operation. This technique is more applicable to estimating the excess delay of each multipath for power-line channel because the impulse response of power line channel has the first path with the smallest attenuation. The simulation results show that we can get the high resolution time delay and the attenuation factors corresponding to the multipaths. The estimated time delays and attenuation factors will facilitate the analysis of the PLC channel with multipaths.