Load forecasting in short term is very important to economic dispatch and safety assessment of power system. Although existing load forecasting in short-term algorithms have reached required forecast accuracy, most of the forecasting models are black boxes and cannot be constructed to display mathematical models. At the same time, because of the abnormal load caused by the failure of the load data collection device, time synchronization, and malicious tampering, the accuracy of the existing load forecasting models is greatly reduced. To address these problems, this article proposes a Short-Term Load Forecasting algorithm by using Improved Gene Expression Programming and Abnormal Load Recognition (STLF-IGEP_ALR). First, the Recognition algorithm of Abnormal Load based on Probability Distribution and Cross Validation is proposed. By analyzing the probability distribution of rows and columns in load data, and using the probability distribution of rows and columns for cross-validation, misjudgment of normal load in abnormal load data can be better solved. Second, by designing strategies for adaptive generation of population parameters, individual evolution of populations and dynamic adjustment of genetic operation probability, an Improved Gene Expression Programming based on Evolutionary Parameter Optimization is proposed. Finally, the experimental results on two real load datasets and one open load dataset show that compared with the existing abnormal data detection algorithms, the algorithm proposed in this article have higher advantages in missing detection rate, false detection rate and precision rate, and STLF-IGEP_ALR is superior to other short-term load forecasting algorithms in terms of the convergence speed, MAE, MAPE, RSME, and
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Gene Expression Programming Coupled with Unsupervised Learning: A Two-Stage Learning Process in Multi-Scale, Short-Term Water Demand Forecasts