Building energy assessment models are considered to be one of the most informative methods in building energy efficiency design, and most of the current building energy assessment models have been developed based on machine learning algorithms. Deep learning models have proved their effectiveness in fields such as image and fault detection. This paper proposes a deep learning energy assessment framework with interpretability to support building energy efficiency design. The proposed framework is validated using the Commercial Building Energy Consumption Survey dataset, and the results show that the wrapper feature selection method (Sequential Forward Generation) significantly improves the performance of deep learning and machine learning models compared with the filtered (Mutual Information) and embedded (Least Absolute Shrinkage and Selection Operator) feature selection algorithms. Moreover, the Deep Forest model has an R² of 0.90 and outperforms the Deep Multilayer Perceptron, the Convolutional Neural Network, the Backpropagation Neural Network, and the Radial Basis Function Network in terms of prediction performance. In addition, the model interpretability results reveal how the features affect the prediction results and the contribution of the features to the energy consumption in a single building sample. This study helps building energy designers assess the energy consumption of new buildings and develop improvement measures.

Energy consumption forecasting plays an important role in energy management, conservation, and optimization in manufacturing companies. Aiming at the tin smelting process with multiple types of energy consumption and a strong coupling with energy consumption, the traditional prediction model cannot be applied to the multi-output problem. Moreover, the data collection frequency of different processes is inconsistent, resulting in few effective data samples and strong nonlinearity. In this paper, we propose a multi-kernel multi-output support vector regression model optimized based on a differential evolutionary algorithm for the prediction of multiple types of energy consumption in tin smelting. Redundant feature variables are eliminated using the distance correlation coefficient method, multi-kernel learning is introduced to improve the multi-output support vector regression model, and a differential evolutionary algorithm is used to optimize the model hyperparameters. The validity and superiority of the model was verified using the energy consumption data of a non-ferrous metal producer in Southwest China. The experimental results show that the proposed model outperformed multi-output Gaussian process regression (MGPR) and a multi-layer perceptron neural network (MLPNN) in terms of measurement capability. Finally, this paper uses a grey correlation analysis model to discuss the influencing factors on the integrated energy consumption of the tin smelting process and gives corresponding energy-saving suggestions.

Aiming at the detection problem of blockage in urban water supply pipelines and drainage pipelines, also the problem to distinguish commonly used pipe components such as lateral connection from the actual blocking conditions. A method based on dual-tree complex wavelet transform and Safe Semi-Supervised Support Vector Machine for blockage recognition is proposed in this paper. The first step of this method is to decompose the acoustic signals obtained from the pipeline by the dual-tree complex wavelet transform, and then convert the acquired components into Sound pressure level. Secondly, the pulse factor and the average acoustic energy density are extracted respectively from the effective components as acoustical features. Finally, the S4VM classifier is applied to cluster and label the untrained data, furthermore the different degree of the blocking is able to identify as well as the pipe components.

Targeting the challenge of determining the degree of blockage in buried pipelines and the difficulty of effectively extracting blockage features, a blockage detection method integrating variational mode decomposition (VMD) and information gain is proposed. Acoustic impulse response signals were obtained by deconvolving the output signals of the system, which were then subjected to VMD to obtain 12 components in different frequency ranges. Next, information gain (IG) was introduced to characterize the 12 components quantitatively, through which the components containing rich information about the pipe conditions were selected out. Meanwhile, sound pressure level conversion was performed on the selected components to amplify any changes in the sound field. Finally, the root mean square entropy (RMSE) was calculated to constitute the feature eigenvectors, which were input into Random Forests (RF) classifier for defect identification of pipeline. As the experimental results demonstrate, the proposed method is capable of determining the degree of blockage effectively in the running state. Meanwhile, it can also eliminate the interference of functional parts such as lateral connections during the identification process, thereby improving the identification accuracy. The present study has shown both theoretical significance and application value in the field of defect detection and recognition.

Regarding the active acoustical method of determining the blocking status in buried pipe using low frequency sound waves as excitation, it has difficulties in distinguishing the different degrees of blocking and eliminating the interference from the pipe components such as lateral connection, a novel approach to identify the blocking fault using the Sparse Representation for Classification (SRC) enhanced by variational mode decomposition was proposed in this paper. Firstly, the acoustic signals collected in the pipeline were decomposed by variational mode decomposition, then sample entropy characteristic parameters were extracted from each component. Secondly, the feature vectors that can effectively represent the signals were used to construct the dictionary and the sparse representation classifier. The experimental results had shown that the method could effectively identify the blocking status in different degrees, the lateral connection could also be discriminated accurately in the pipeline under a small number of samples, which has certain practical value for engineering applications.