Intelligent diagnosis is an important means of ensuring the safe and stable operation of chillers driven by big data. To address the problems of input feature redundancy in intelligent diagnosis and reliance on human intervention in the selection of model parameters, a chiller fault diagnosis method was developed in this study based on automatic machine learning. Firstly, the improved max-relevance and min-redundancy algorithm was used to extract important feature information effectively and automatically from the training data. Then, the long short-term memory (LSTM) model was used to mine the temporal correlation between data, and the genetic algorithm was employed to train and optimize the model to obtain the optimal neural network architecture and hyperparameter configuration. Finally, a transient co-simulation platform for building chillers based on MATLAB as well as the Engineering Equation Solver was built, and the effectiveness of the proposed method was verified using a dynamic simulation dataset. The experimental results showed that, compared with traditional machine learning methods such as the recurrent neural network, back propagation neural network, and support vector machine methods, the proposed automatic machine learning algorithm based on LSTM provides significant performance improvement in cases of low fault severity and complex faults, verifying the effectiveness and superiority of this method.

Monitoring geotechnical structures and providing real-time early warning is a key measure to mitigate the impacts of disasters (slope slip, subsidence, dam deformation, bridge settlement, etc.). The fiber Bragg grating (FBG) flexible sensor, developed by the combination of flexible material and an FBG sensor, is widely used in geotechnical engineering health monitoring due to its excellent performance. The flexible sensor can perform regional and quasi-distributed measurements of the displacement field of the measured structure, and accurately reflect the operating state of the engineering structure. However, in practical engineering applications, factors such as the strain-transfer rate between the flexible substrate and sensing points, the displacement reconstruction algorithm, and the arrangement interval of the sensing points can cause measurement error, which, in turn, leads to a decrease in the displacement-measurement accuracy. In this paper, the following analysis is performed by means of theoretical derivation and model establishment. The influence of the length, width, and thickness of the cemented layer, the shear modulus of the flexible substrate, and the radius of the groove on the strain-transfer rate were analyzed, and the referential parameters were determined. The displacement reconstruction algorithm is essentially a recursive algorithm, which inevitably introduces cumulative error; the relationship between the layout interval of the sensing points and the measurement error is discussed. Considering the fabrication cost of the sensor and the allowable range of error, a sensing-point-layout interval of 100 mm was chosen. The feasibility and effectiveness of the simulation theory were verified by carrying out deformation-sensing experiments on the developed FBG flexible sensor. The research results can theoretically guide the packaging and fabrication of the FBG flexible sensor, thereby improving the measurement accuracy of the flexible sensor for the measured structure.

Amid rising load volatility and uncertainty, demand-side resources with regulation capabilities are increasingly engaged at scale in ancillary service markets, facilitating sustainable peak load mitigation and alleviating grid stress while reducing reliance on carbon-intensive peaking plants. This study examines the integration of electric vehicles (EVs) in peak regulation, proposing a multi-stage operational strategy framework grounded in the analysis of EV power and energy response constraints to promote both economic efficiency and environmental sustainability. The model holistically accounts for temporal charging and discharging behaviors under diverse incentive mechanisms, incorporating user response heterogeneity alongside multi-period market peak regulation demands while supporting clean transportation adoption. An optimization model is formulated to maximize aggregator revenue while enhancing grid sustainability and is solved via MATLAB(2021b) and CPLEX(20.1.0). The simulation outcomes reveal that the discharge-based demand response (DBDR) strategy elevates aggregator revenue by 42.6% and enhances peak regulation margins by 19.2% relative to the conventional charge-based demand response (CBDR). The hybridization of CBDR and DBDR yields a threefold revenue increase and a 28.7% improvement in peak regulation capacity, underscoring the efficacy of a joint-response approach in augmenting economic returns, grid flexibility, and sustainable energy management.

Unmanned aerial vehicle (UAV) could be seen as an importanttechnique to efficiently support wide-area massive Internet of Things(IoT) access because of inherent properties, such as flexibility,mobility, and adaptive altitude.