关键词: 智能电网, 智能制造, 短期负荷预测, 外加输入的自回归滑动平均模型, 量子进化算法, 云计算

Abstract: With the combination of Internet of Things (IoT) and intelligent manufacturing,amount of manufacturing data had been generated.To realize the power load forecasting of intelligent manufacturing based on big data,a smart IoT cloud computing platform was designed and implemented to provide quick response of bidirectional communication and control between users and smart IoT.A new model based on improved Auto-Regressive and Moving Average with eXogenous input (ARMAX) model was proposed for short-term dynamic load forecasting combining the intelligent sensing devices in the platform and the historical load and weather information as the input variables of forecasting model.An improved real-coded quantum evolution algorithm was used for parameter estimation of new forecasting model to improve the precision of dynamic load forecasting.Compared with the actual load data of intelligent manufacturing and the forecasting results of traditional methods,the experimental results showed that the proposed scheme and algorithm could effectively improve the accuracy and precision of short-term load forecasting in intelligent manufacturing,and promote expedite load forecasting by parallel calculation.

Key words: smart Internet of things, intelligent manufacturing, dynamic load forecasting, auto-regressive and moving average with exogenous input model, quantum evolutionary algorithm, cloud computing