Model predictive control is theoretically suitable for optimal control of the building, which provides a framework for optimizing a given cost function (e.g., energy consumption) subject to constraints (e.g., thermal comfort violations and HVAC system limitations) over the prediction horizon. However, due to the buildings’ heterogeneous nature, control-oriented physical models’ development may be cost and time prohibitive. Data-driven predictive control, integration of the “Internet of Things”, provides an attempt to bypass the need for physical modeling. This work presents an innovative study on a data-driven predictive control (DPC) for building energy management under the four-tier building energy Internet of Things architecture. Here, we develop a cloud-based SCADA building energy management system framework for the standardization of communication protocols and data formats, which is favorable for advanced control strategies implementation. Two DPC strategies based on building predictive models using the regression tree (RT) and the least-squares boosting (LSBoost) algorithms are presented, which are highly interpretable and easy for different stakeholders (end-user, building energy manager, and/or operator) to operate. The predictive model’s complexity is reduced by efficient feature selection to decrease the variables’ dimensionality and further alleviate the DPC optimization problem’s complexity. The selection is dependent on the principal component analysis (PCA) and the importance of disturbance variables (IoD). The proposed strategies are demonstrated both in residential and office buildings. The results show that the DPC-LSBoost has outperformed the DPC-RT and other existing control strategies (MPC, TDNN) in performance, scalability, and robustness.

CO₂ emissions are major drivers of climate change, causing global warming, extreme weather, and biodiversity loss. They disrupt ecosystems, deplete resources, and threaten public health and economic stability. Reducing CO₂ emissions is essential for climate stability and sustainability. This study explores the complex relationships between CO₂ emissions and factors such as the transportation sector, electricity consumption, foreign direct investment (FDI), international trade, and gross domestic product (GDP). The focus is on small- and medium-sized enterprises (SME) in Pakistan. Using time series data from 2000 to 2022. This study applies advanced econometric techniques, including nonlinear autoregressive distributed lag (NARDL), dynamic ordinary least squares (DOLS), and fully modified ordinary least squares (FMOLS). The findings highlight that increased electricity consumption, international trade, transportation activities, and FDI contribute to higher CO₂ emissions. However, FDI can also help reduce emissions, particularly through investments in green technologies. This study emphasizes the importance of transitioning to renewable energy and adopting sustainable practices across sectors such as electricity, trade, and transportation. Specifically, transportation and electricity consumption were found to significantly impact CO₂ emissions, with a 10% increase in transportation activities resulting in a 5% rise in emissions. Conversely, FDI can reduce emissions by approximately 3% per unit of investment, largely due to green technology adoption. Additionally, integrating renewable energy and energy-efficient technologies in transportation can lead to a 20% reduction in emissions. Policymakers and experts must prioritize strategies that promote renewable energy adoption and integrate sustainable practices to reduce CO₂ emissions and ensure long-term environmental sustainability. This research is innovative in its analysis of the interconnected effects of electricity consumption, trade, transportation, and FDI on CO₂ emissions. By applying sophisticated econometric methods, it highlights the potential of FDI, particularly green investments, to mitigate environmental damage. This study, focusing on Pakistan, offers insights into how economic growth can be balanced with environmental sustainability.

Accurate traffic flow detection plays a critical role in intelligent traffic control systems. However, conventional fixed video detection devices often face challenges such as occlusion and overlap in high-density traffic scenarios, which leads to distortions in vehicle detection. To address this issue, it is essential to obtain precise vehicle data as a reliable reference for managing traffic flow during peak periods. In this paper, we propose an intelligent detection scheme using an improved YOLOv8n target recognition algorithm combined with a ByteTrack multi-target tracking algorithm. A collaborative unmanned aerial vehicle (UAV) collaborative detection framework is also established, integrating UAVs and fixed detection devices to work in tandem. Such a multi-UAV collaborative data acquiring system is designed for efficient, continuous, and uninterrupted operation, employing a three-drone rotational detection strategy. UAVs offer additional flexibility and coverage in obtaining vehicle data. However, limited power could be an essential challenge to the system’s wireless physical link stability and safety. To overcome power limitations during UAV collaboration, a wireless charging (WC) system is introduced, enabling automatic constant current–constant voltage (CC-CV) switching and preventing damage from accidental data link disabling. This collaborative traffic data acquiring and transmission system ensures a stable power supply for UAVs during high-density traffic periods, supporting their reliable UAV collaborative wireless data link. Experimental results show that the collaborative detection architecture combined with wireless charging can achieve high detection accuracy, with the recognition accuracy remaining between 0.95 and 0.99.

The corona-stabilized switch has the potential to be a high repetition rate pulsed-power switching device, but there has been limited investigation into its repetitive breakdown stability and insulation recovery characteristics. Repetitive breakdowns of gas are characterized by a memory effect, where the subsequent breakdown process is inevitably influenced by the preceding one. However, there are still some issues that require further exploration in the current research on the mechanism of memory effect on repetitive breakdown characteristics. To clarify the factors and mechanisms that affect the repetitive breakdowns of corona-stabilized switches, this paper introduced optical observation methods into the experimental investigation. Through optical–electrical coupled diagnosis, the repetitive breakdown stability and insulation recovery performance of corona-stabilized switches under different working conditions and repetition frequencies were analyzed. The monotonic promoting effect of corona stabilization on switch insulation strength recovery is proposed as well as the non-monotonic and complex regulatory mechanism of corona stabilization on repetitive breakdown stability. The research results provide a theoretical and practical basis for clarifying the mechanism of repetitive corona-stabilized breakdowns and optimizing the design of corona-stabilized switches.

Most of the existing differential methods focus on the differential effect and do not make full use of the differential link’s filtering effect of reducing order and smoothing. In Proportion Integral Differential (PID) control, the introduction of differential can improve the dynamic performance of the system. However, the actual differential (containing differential gain) will be subject to the impact of high-frequency noises. Therefore, this paper proposes a differential with filtering function, which has weak effect on noise amplification, and strong effect on reducing order and smoothing. Firstly, a discrete differentiator was constructed based on the Newton interpolation, and the concept of “algorithm bandwidth” was defined to ensure the differential effect. Then, the proposed algorithm was used to design a new PID controller with feedforward filtering function. In the experiments, the proposed PID controller is applied to a high-performance hot water supply system. The result shows that the system obtains better control quality. It verifies that the proposed PID controller has a feedforward filtering function and can effectively remove high-frequency noise.