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In this paper, a dynamic ensemble method is proposed to forecast the residential short-term load accurately. The main idea is to utilize the state-space approaches to dynamically adjust the weight coefficients used to combine the base models. The dynamic ensemble method is divided into two stages. In the first stage, the least square method is utilized to estimate the weight coefficients. In the second stage, the particle filter is employed to exclude the estimation error in the first stage and update the weight coefficients dynamically to improve the ensemble accuracy. Then, three heterogeneous models (i.e., vector autoregression model, Gaussian process regression model, and the long short-term memory neural network) are employed as the base models and combined based on the weight coefficients to forecast the residential load. The numerical tests are conducted considering two scenarios in two public datasets to investigate the performance of the proposed method. The first scenario assumes the base models are accurate and stable, while the second scenario considers the dynamics of the base models. Root mean square error, mean absolute error, and mean absolute percentage error are used to evaluate the performances. These indicators show that the proposed method outperforms in both scenarios and datasets.

Time-of-use pricing of electric energy, as an important part of the national policy of energy conservation and emission reduction, requires accurate electric energy data as support. However, due to various reasons, the electric energy data are often missing. To address this thorny problem, this paper constructs a CNN and GRU-based recovery model (RCNN-BiGRU) for electric energy data by taking the missing data as the output and the historical data of the neighboring moments as the input. Firstly, a convolutional network with a residual structure is used to capture the local dependence and periodic patterns of the input data, and then a bidirectional GRU network utilizes the extracted potential features to model the temporal relationships of the data. Aiming at the difficult selection of network structure parameters and training process parameters, an accelerated adaptive differential evolution (AADE) algorithm is proposed to optimize the electrical energy data recovery model. The algorithm designs an accelerated mutation operator and at the same time adopts an adaptive strategy to set the two key parameters. A large amount of real grid data are selected as samples to train the network, and the comparison results verify that the proposed combined model outperforms the related CNN and GRU networks. The comparison experimental results with other optimization algorithms also show that the AADE algorithm proposed in this paper has better data recovery performance on the training set and significantly better performance on the test set. [ABSTRACT FROM AUTHOR]

Tunnels play a vital role in enhancing traffic flow and supporting public transportation systems. However, the discharge of polluted air and waste heat from vehicles passing through tunnels significantly raises the temperature inside, presenting challenges in terms of occupant comfort, tunnel safety, and infrastructure integrity. Therefore, ensuring proper temperature control is essential for their efficient operation. This study aims to investigate the phenomenon of temperature rise in ultra-long tunnels during normal operations, as limited research has been conducted in this area. The Shanghai Yangtze River Tunnel serves as a case study, utilizing temperature and air velocity data collected throughout the year (2021) from the management company. The analysis reveals that the temperature distribution near the tunnel exit is influenced by outdoor temperature fluctuations and traffic volume. The highest temperatures occur on 25 August (39.74 °C) during peak traffic hours. On-site measurements of tunnel temperature, humidity, and air velocity during winter and summer seasons yield the following results. During winter, the air temperature and wall temperature inside the tunnel experience significant increases along its length. The air temperature rises by approximately 11 °C from the entrance to the exit, while the wall temperature increases by about 15 °C. In contrast, during summer, the air temperature only rises by 2.7 °C, and the wall temperature increases by around 3 °C. Consequently, the humidity decreases along the tunnel, and this decrease is correlated with the magnitude of temperature increase. Furthermore, measurements of air velocity indicate that natural and traffic-induced winds contribute to the overall airflow inside the tunnel. A temperature data logger installed in the tunnel recorded temperature changes during the period of pandemic lockdown and subsequent recovery, spanning the spring and summer seasons. During the lockdown period, there was a relatively small increase in temperature along the tunnel, suggesting that vehicle heat dissipation is the primary factor contributing to temperature rise inside. Additionally, a method is proposed to predict the cross-sectional temperature of the tunnel using measured air velocities. [ABSTRACT FROM AUTHOR]