Short-term load forecasting (STLF) with excellent precision and prominent efficiency plays a significant role in the stable operation of power grid and the improvement of economic benefits. In this paper, a novel model based on data mining and deep learning is proposed. Firstly, the preprocessing of data includes normalization of historical load, and fuzzification of influencing factors (meteorological factors, date types and economy) based on Pearson correlation coefficient (PCC). Secondly, kernel fuzzy c-means (KFCM) modified by particle swarm optimization (PSO-KFCM) algorithm clusters the daily load curve. In the clustering experiments, the within-cluster sum of squared error (SSE) index is presented to determine the number of clusters and the clustering validity has a 31.9% enhancement compared with the traditional FCM algorithm. Thirdly, the cosine similarity establishes the resemblance between the prediction date and each cluster, and the similar cluster is determined according to the principle of maximum similarity. Finally, a multivariate and multi-step hybrid model MMCNN-LSTM based on convolution neural network (CNN) and long short-term memory (LSTM) neural network is proposed to forecast the load in following 24 hours, in which similar cluster data is applied to training set. To demonstrate the effectiveness of proposed integrated technique, the accuracy has been verified in three predictive experiments. The fruitful results indicated that the average mean absolute percent error (MAPE) in the entire test set was only 1.34%, a 3.02% reduction compared to a single LSTM.

The vibration signals of rolling bearing are often highly nonstationary and nonlinear, and consequently it is not accurate to extract and identify the characteristics of these signals by the traditional methods. In order to improve the performance on the feature extraction from bearing signals and the accuracy of the diagnosis, it requires effective signal processing and diagnose algorithms. In this paper, a new fault diagnosis algorithm which combines complementary ensemble empirical mode decomposition (CEEMD), probabilistic neural network (PNN) and particle swarm optimization (PSO) algorithm optimized by improved linear decreasing weight (LDW) algorithm is proposed. In this method, firstly the vibration signals are decomposed into a number of Intrinsic Mode Functions (IMFs) by the CEEMD algorithm since it has good adaptive ability to nonstable signals and can effectively extract fault features. Then the improved LDWPSO algorithm is introduced to solve the problem that the selection of smoothing factor in PNN model is arbitrary and uncertain. Finally, train and diagnose the fault types of rolling bearing using the LDWPSO-PNN model. The proposed method is verified by the experimental datasets. The results indicate that the method can extract the feature vectors of the vibration signals and distinguish them effectively.

This paper proposes a short-term electric load forecasting method combining convolutional neural networks and gated recurrent units with an attention mechanism. By integrating CNNs and GRUs, the method can fully leverage the strengths of CNNs in feature extraction and the advantages of GRUs in sequence modeling, enabling the model to comprehend signal data more comprehensively and effectively extract features from sequential data. The introduction of the attention mechanism allows the traditional model to dynamically focus on important parts of the input data while ignoring the unimportant parts. This capability enables the model to utilize input information more efficiently, thereby enhancing model performance. This paper applies the proposed model to a dataset comprising regional electric load and meteorological data for experimentation. The results show that compared with other common models, the proposed model effectively reduces the mean absolute error and root mean square error (121.51 and 263.43, respectively) and accurately predicts the short-term change in regional power load.

The vibration signals of rolling bearing are often non-stationary and non-linear, and consequently it is much more difficult to extract the deep characteristics in the time domain. In this paper, a new fault diagnosis method is proposed to identify the fault types of rolling bearings combined the benefits of the modified ensemble empirical mode decomposition (MEEMD), quantum particle swarm optimization (QPSO) and least squares support vector machine (LSSVM) algorithms. In this method, the vibration signals are decomposed by the MEEMD algorithm to obtain the intrinsic mode function (IMF) components. After normalizing the energy moment characteristics of each IMF component, the feature vectors can be obtained and conveniently input into the LSSVM model optimized by the QPSO algorithm to perform training and identification. It can effectively improve the performance on decomposition and extraction of vibration signals, and further improve the accuracy of the fault diagnosis. The proposed method is verified by the results of the experiments. It shows that this technique can extract the characteristics of the vibration signals effectively and identify them accurately.