Understanding electrical load profiles and detecting anomaly behaviors are important to the smart grid system. However, current load identification and anomaly analysis are based on static analysis, and less consideration is given to anomaly findings under load change conditions. This paper proposes a deep variational autoencoder network (DVAE) for load profiles, along with anomaly analysis services, and introduces auto-time series data updating strategies based on sliding window adjustment. DVAE can help reconstruct the load curve and measure the difference between the original and the newer curve, whose measurement indicators include reconstruction probability and Pearson similarity. Meanwhile, the design of the sliding window strategy updates the data and DVAE model in a time-series manner. Experiments were carried out based on datasets from the U.S. Department of Energy and from Southeast China. The results showed that the proposed services could result in a 5% improvement in the AUC value, which helps to identify the anomaly behavior.

The construction of smart grids has greatly changed the power grid pattern and power supply structure. For the power system, reasonable power planning and demand response is necessary to ensure the stable operation of a society. Accurate load prediction is the basis for realizing demand response for the power system. This paper proposes a Pre-Attention-CNN-GRU model (PreAttCG) which combines a convolutional neural network (CNN) and gate recurrent unit (GRU) and applies the attention mechanism in front of the whole model. The PreAttCG model accepts historical load data and more than nine other factors (including temperature, wind speed, humidity, etc.) as input. The attention layer and CNN layer effectively extract the features and weights of each factor. Load forecasting is then performed by the prediction layer, which consists of a stacked GRU. The model is verified by industrial load data from a German dataset and a Chinese dataset from the real world. The results show that the PreAttCG model has better performance (3~5% improvement in MAPE) than both LSTM with only load input and LSTM with all factors. Additionally, the experiments also show that the attention mechanism can effectively extract the weights of relevant factors affecting the load data.

Smart grid system can obtain users' daily load data, and by clustering, we can get users' load profiles to divide them into industrial, commercial and residential types. Load data has the characteristic of changing periodically. Within a period, the load profiles are relatively stable. However, load profiles often changes significantly according to reasons like holidays and season changing. When conducting a continuous clustering task for consecutive days, traditional clustering algorithms cannot consider the time-dimension features into analysis, which may make clustering results be very different even if user behaviors are almost the same. Evolutionary clustering (EC) can be taken into consideration. EC doesn't ignore historical clustering results and makes results more stable in a period. However, when dramatic changes happen in user behaviors, the quality of EC's clustering results will decrease significantly. This paper proposed an optimized evolutionary clustering algorithm: Timestamp-Based Self-Adaptive Evolutionary Clustering (TBSAEC). TBSAEC is based on evolutionary clustering, and takes a heuristic approach to pick the evolutionary parameter to maximize the total quality. TBSAEC maintains the stability of continuous-time clustering results while better adapting to changes in user behaviors. Besides, TBSAEC optimize the running efficiency of the algorithm by picking samples in equal portions from historical data instead of the whole data. We applied TBSAEC to the load data of a certain region in east China in 2015, and the results showed that TBSAEC is 3% to 9% higher than the ordinary evolutionary clustering algorithm in total quality, and 87% faster in running time.