Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. This study presents a hybrid algorithm that combines similar days (SD) selection, empirical mode decomposition (EMD), and long short-term memory (LSTM) neural networks to construct a prediction model (i.e., SD-EMD-LSTM) for short-term load forecasting. The extreme gradient boosting-based weighted k-means algorithm is used to evaluate the similarity between the forecasting and historical days. The EMD method is employed to decompose the SD load to several intrinsic mode functions (IMFs) and residual. Separated LSTM neural networks were also employed to forecast each IMF and residual. Lastly, the forecasting values from each LSTM model were reconstructed. Numerical testing demonstrates that the SD-EMD-LSTM method can accurately forecast the electric load.

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The term 'risk' has been used in many different applications. The notion of the risk society has attracted considerable attention within the academic literature. Kinds of risk management paradigm are introduced by scholars. Different paradigm is focus on different type of risk. All paradigms have the similar aspects, however keep keystone into different area. The aim of the paper is analyzed the current risk theory and risk management framework, the argument base on the review of the current literature. From a review of the risk literature, there is a lack of an integrative theory that can address and evaluate all risk properties, the evaluation of the popular risk management is conducted. KEYWORD: risk analysis; risk management; risk management framework.