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An improved Grey-based approach for electricity demand forecasting

机译：一种改进的基于灰色的电力需求预测方法

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The aim of this project is to develop an online electricity demand predictor. In this paper, we present an improved Grey-based prediction algorithm to forecast a very-short-term electric power demand for the demand-control of electricity. We adopted Grey prediction as a forecasting means because of its fast calculation with as few as four data inputs needed. However, our preliminary study shows that the general Grey model, GM(1,1) is inadequate to handle a volatile electrical system. The general GM(1,1) prediction generates the dilemmas of dissipation and overshoots. In this study, the prediction is improved significantly by applying the transformed Grey model and the concept of average system slope. The adaptive value of a in the Grey differential equation is obtained quickly with the average system slope technique. The present intelligent Grey-based electric demand-control system is able to provide an instrument to save operation costs for high energy consuming enterprises. In such a way, the wastage of electric consumption can be avoided. That is, it is another achievement of virtual electric power plant.
机译：该项目的目的是开发在线电力需求预测器。在本文中，我们提出了一种改进的基于灰色的预测算法，以预测非常短期的电力需求，以实现电力需求控制。我们将格雷预测作为一种预测手段，因为它的快速计算仅需要四个数据输入。但是，我们的初步研究表明，通用的灰色模型GM（1,1）不足以处理易失性电气系统。通用GM（1,1）预测会产生耗散和过冲的难题。在这项研究中，通过应用变换的灰色模型和平均系统斜率的概念，显着改善了预测。使用平均系统斜率技术可以快速获得格雷微分方程中a的自适应值。当前的基于灰色的智能电力需求控制系统能够为高能耗企业提供一种节省运营成本的工具。这样，可以避免浪费电力。也就是说，这是虚拟电厂的另一项成就。

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