Considering the lack of effective trend feature descriptors in existing methods， financial technical indicators such as Vertical Horizontal Filter （VHF） and Moving Average Convergence/Divergence （MACD） were introduced into power data analysis. An anomaly detection algorithm and a load forecasting algorithm using financial technical indicators were proposed. In the proposed anomaly detection algorithm， the thresholds of various financial technical indicators were determined based on statistics， and then the abnormal behaviors of user power consumption were detected using threshold detection. In the proposed load forecasting algorithm， 14 dimensional daily load characteristics related to financial technical indicators were extracted， and a Long Shot-Term Memory （LSTM） load forecasting model was built. Experimental results on industrial power data of Hangzhou City show that the proposed load forecasting algorithm reduces the Mean Absolute Percentage Error （MAPE） to 9.272%， which is lower than that of Autoregressive Integrated Moving Average （ARIMA）， Prophet and Support Vector Machine （SVM） algorithms by 2.322， 24.175 and 1.310 percentage points， respectively. The results show that financial technical indicators can be effectively applied to power data analysis.

A novel kernel-based non-negative sparse representation (KNSR) method was presented for face recognition. The contributions were mainly three aspects: First, the non-negative constraints on representation coefficients were introduced into the Sparse Representation (SR) and the kernel function was exploited to depict non-linear relationships among different samples, based on which the corresponding objective function was proposed. Second, a multiplicative gradient descent method was proposed to solve the proposed objective function, which could achieve the global optimum value in theory. Finally, local binary feature and the Hamming kernel were used to model the non-linear relationships among face samples and therefore achieved robust face recognition. The experimental results on some challenging face databases demonstrate that the proposed algorithm has higher recognition rates in comparison with algorithms of Nearest Neighbor (NN), Support Vector Machine (SVM), Nearest Subspace (NS), SR and Collaborative Representation (CR), and achieves about 99% recognition rates on both YaleB and AR databases.