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 R 2 .

中文翻译：

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使用改进的 GEP 和异常负载识别进行短期负载预测

短期负荷预测对电力系统的经济调度和安全评估具有重要意义。尽管现有的短期算法中的负荷预测已经达到了所需的预测精度，但大多数预测模型都是黑匣子，无法构建显示数学模型。同时，由于负荷数据采集设备故障、时间同步、恶意篡改等原因导致的负荷异常，大大降低了现有负荷预测模型的准确性。针对这些问题，本文提出了一种使用改进的基因表达编程和异常负载识别（STLF-IGEP_ALR）的短期负载预测算法。首先，提出了基于概率分布和交叉验证的异常负载识别算法。通过分析负载数据中行和列的概率分布，利用行和列的概率分布进行交叉验证，可以更好地解决异常负载数据中对正常负载的误判。其次，通过设计种群参数自适应生成策略、种群个体进化策略和遗传操作概率动态调整策略，提出了一种基于进化参数优化的改进基因表达规划。最后，在两个真实负载数据集和一个开放负载数据集上的实验结果表明，与现有的异常数据检测算法相比，本文提出的算法在漏检率、误检率和准确率方面具有更高的优势，R 2.