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Achieving precision in the prediction of buildings' dynamic heat load is crucial for the advancement of smart heating systems. This research highlights the urgent need to enhance the accuracy of models predicting dynamic heat load. Through literature review, distinguished machine learning and regression algorithms were chosen to formulate prediction models. These models employ a data time-step adaptive strategy, a physics-guided loss function, and fundamental principles of heat transfer. Optimization algorithms of a mathematical nature were utilized to fine-tune the parameters and the framework of long short-term memory (LSTM) and multi-layer perceptron (MLP) models. An analytical comparison was undertaken between physics-guided models and those not guided by physics. Principal conclusions are: 1) Pelican optimization algorithm (POA)-LSTM model emerges as superior in heat load prediction accuracy of an office building, with percentage errors for actual and simulated datasets ranging from −6.7 % to 5.8 % and −5.2 %–4.5 %, respectively, and the mean absolute percentage error (MAPE) standing at 2.3 % and 1.3 %. 2) The linear regression model exhibits the lowest precision, with a MAPE of 17.5 % and 4.0 % for the 7-day prediction results in the actual and simulated datasets, respectively. These findings provide support for improving heat load prediction in heating systems. • The authors have implemented a total of 20 physically-guided models for predicting building thermal load, among which the POA-LSTM model demonstrates the highest accuracy. • The disparities in thermal load prediction accuracy between the physically-guided machine learning models and the physically-guided mathematical regression models have been identified. • The accuracy of thermal load prediction was compared between the five physical guidance models proposed in this paper and the five non-physical guidance models mentioned in other papers. [ABSTRACT FROM AUTHOR]

Timely diagnosis and defect warning in power systems, related to the probability of a fault occurring, may be achieved by examining the correlations between various factors and evaluating the collected information. To check the performance of the machine learning models, a large amount of data is required in various operating conditions to feed as input to the algorithms. The primary focus of the conducted work is on the defect warning process, with additional studies discussing fault identification, type determination, and location. The stacking-based method is considered the main learning approach; however, the separate outcomes for the three single base methods, i.e., RF, SVM, and XGBoost, along with some other popular and strong algorithms have also been presented. In the warning study section, the proposed stacking approach achieved a minimum accuracy of 99.31 % while taking into consideration the noisy samples, and it has achieved a maximum accuracy of 100 % for all weather conditions with noise-free data. The fault identification component achieves an accuracy of 100 %, whereas the type determination part achieves an accuracy of 99.98 %. More details and results are provided in the paper. The modeling and simulations in this work were implemented in Python and MATLAB. • A defect warning procedure for transmission lines are introduced. • The effect of weather situations as external factors is regarded. • A stacking-based machine learning approach is proposed to detect and classify the defects. • A definite fault tracking process utilizing the stacking-based method is analyzed. • The accuracy of predictions has enhanced compared to individual models. [ABSTRACT FROM AUTHOR]

In this paper, a comparative study on the state of charge (SOC) estimation of the lithium-rich manganese-based battery (LRMB) has been conducted by systematically considering the equivalent circuit model (ECM), aging state and algorithms. The results show that the first-order RC model combined with the Extended Kalman filter (EKF) is more suitable for the SOC estimation of the LRMB when the battery decays less severely. The maximum value of root mean square error of SOC estimation error no exceeds 1.6 % under various dynamic operating conditions, which can well meet the practical application. Besides, the first-order model based EKF algorithm occupies least computing resource and simultaneously possesses the best convergence velocity against the initial SOC value deviation. When the LRMB suffers a severe aging, the SOC estimation performance decreases significantly with the max error exceeding 6 %, to overcome this, a new algorithm combined deep learning and Extend Kalman filter (EKF) is proposed, which exhibit high SOC estimation accuracy with the RMSE less than 0.16 %. In summary, a systematic study about the SOC estimation of LRMB has been carried out, which can provide great guidance for the future engineering application of LRMB. • Various Bayesian filtering algorithms and neural networks are systematically compared. • The aging states of LRMB are taken into account. • The different types of ECMs are adopted in the SOC estimation of LRMB. [ABSTRACT FROM AUTHOR]