Load forecasting is one of the major challenges of power system operation and is crucial to the effective scheduling for economic dispatch at multiple time scales. Numerous load forecasting methods have been proposed for household and commercial demand, as well as for loads at various nodes in a power grid. However, compared with conventional loads, the uncoordinated charging of the large penetration of plug-in electric vehicles is different in terms of periodicity and fluctuation, which renders current load forecasting techniques ineffective. Deep learning methods, empowered by unprecedented learning ability from extensive data, provide novel approaches for solving challenging forecasting tasks. This research proposes a comparative study of deep learning approaches to forecast the super-short-term stochastic charging load of plug-in electric vehicles. Several popular and novel deep-learning based methods have been utilized in establishing the forecasting models using minute-level real-world data of a plug-in electric vehicle charging station to compare the forecasting performance. Numerical results of twelve cases on various time steps show that deep learning methods obtain high accuracy in super-short-term plug-in electric load forecasting. Among the various deep learning approaches, the long-short-term memory method performs the best by reducing over 30% forecasting error compared with the conventional artificial neural network model.

The low cost inertial navigation system (INS) suffers from bias and measurement noise, which would result in poor navigation accuracy during global positioning system (GPS) outages. Aiming to bridge GPS outages duration and enhance the navigation performance, a deep learning network architecture named GPS/INS neural network (GI-NN) is proposed in this paper to assist the INS. The GI-NN combines a convolutional neural network and a gated recurrent unit neural network to extract spatial features from inertial measurement unit (IMU) signals and track their temporal characteristics. The relationship among the attitude, specific force, angular rate and the GPS position increment is modelled, while the current and previous IMU data are used to estimate the dynamics of the vehicle by GI-NN. Numerical simulations, real field tests and public data tests are performed to evaluate the effectiveness of the proposed algorithm. Compared with the traditional machine learning algorithms, the results illustrate that the proposed method can provide more accurate and reliable navigation solutions in GPS denied environments.

In this paper, a remote sensing method for heart rate (HR) detection is proposed via nearinfrared facial video data, which can be used for the online monitoring of the physiological parameters of the drivers and further analysis so as to enhance the driving safety. The proposed HR detection method is based on an automatic facial tracking algorithm, i.e., the Kanade-Lucas-Tomasi, to transform the facial images into time-series signals. Empirical mode decomposition, bandpass filtering and fast Fourier transformation are applied to the time-series signals for the HR extraction. Indoor experiments in different scenarios are conducted to discuss the involved impact factors, i.e., the distance between the near-infrared camera and the participant, the illumination intensity and the video duration, to acquire the optimal settings for HR measurement. It is more robust and convenient than those of the current commercial devices and webcambased HR measurement approaches, especially in complex illumination environments, such as wearing hat, glasses or makeup, with measurement accuracy 95%. Field experiments are also performed for the prognosis of physical state of the driver.

This paper proposes a noninvasive method of physiological parameter measurement with the collected facial video images for continuous online monitoring. Blood pressure (BP) is one of the important physiological indexes for human health prognoses. Based on the photoplethysmograph (PPG) technology, the variation of the facial skin colour along with the heart beating can thus be captured via the camera. So the related BP information can be extracted from the blind source signal separation. The detailed procedure of the BP measurement from the facial video is explained, including video collection, face tracking, signal pre-processing and BP signal extraction. The systolic and diastolic BP model can be derived based on the radial resonance theory and the involved parameters are fitted via the sampling data. Experiments are performed in different scenarios to discuss the impact factors on the BP detection together with error compensation analysis. The experimental results demonstrate that the proposed BP online measurement can be used as an indicator to monitor the status of the human health continuously.

In the paper, the effect of the charging behaviours of electric vehicles (EVs) on the grid load is discussed. The residential traveling historical data of EVs are analyzed and fitted to predict their probability distribution, so that the models of the traveling patterns can be established. A nonlinear stochastic programming model with the maximized comprehensive index is developed to analyze the charging schemes, and a heuristic searching algorithm is used for the optimal parameters configuration. With the comparison of the evaluation criteria, the multiobjective strategy is more appropriate than the single‐objective strategy for the charging, i.e., electricity price. Furthermore, considering the characteristics of the normal batteries and charging piles, user behaviour and EV scale, a Monte Carlo simulation process is designed to simulate the large‐scale EVs traveling behaviours in long‐term periods. The obtained simulation results can provide prediction for the analysis of the energy demand growth tendency of the future EVs regulation. As a precedent of open‐source simulation system, this paper provides a stand‐alone strategy and architecture to regulate the EV charging behaviours without the unified monitoring or management of the grid.