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首页> 外文期刊>Smart Grid, IEEE Transactions on >Genetic Optimal Regression of Relevance Vector Machines for Electricity Pricing Signal Forecasting in Smart Grids
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Genetic Optimal Regression of Relevance Vector Machines for Electricity Pricing Signal Forecasting in Smart Grids

机译：智能电网电价信号预测的相关向量机遗传最优回归

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摘要

Price-directed demand in smart grids operating within deregulated electricity markets calls for real-time forecasting of the price of electricity for the purpose of scheduling demand at the nodal level (e.g., appliances, machines, and devices) in a way that minimizes energy cost to the consumer. In this paper, a novel hybrid methodology for electricity price forecasting is introduced and applied on a set of real-world historical data taken from the New England area. The proposed approach is implemented in two steps. In the first step, a set of relevance vector machines (RVMs) is adopted, where each RVM is used for individual ahead-of-time price prediction. In the second step, individual predictions are aggregated to formulate a linear regression ensemble, whose coefficients are obtained as the solution of a single objective optimization problem. Thus, an optimal solution to the problem is found by employing the micro-genetic algorithm and the optimized ensemble is employed for computing the final price forecast. The performance of the proposed methodology is compared with performance of autoregressive-moving-average and naïve forecasting methods, as well as to that taken from each individual RVM. Results clearly demonstrate the superiority of the hybrid methodology over the other tested methods with regard to mean absolute error for electricity signal pricing forecasting.
机译：在放松管制的电力市场中运行的智能电网中，以价格为导向的需求要求对电价进行实时预测，以便以最小化能源成本的方式在节点级别（例如电器，机器和设备）安排需求给消费者在本文中，介绍了一种新颖的用于电价预测的混合方法，并将其应用于从新英格兰地区获得的一组现实世界历史数据。建议的方法分两个步骤实施。第一步，采用一组相关矢量机（RVM），其中每个RVM用于单独的提前价格预测。在第二步中，将单个预测汇总起来，以形成线性回归集合，其系数作为单个目标优化问题的解决方案而获得。因此，通过采用微遗传算法找到了该问题的最佳解决方案，并且采用了优化的集成来计算最终价格预测。将所提出的方法的性能与自回归移动平均和幼稚的预测方法的性能进行比较，并与从每个单独的RVM中获得的性能进行比较。结果清楚地表明，就电信号价格预测的平均绝对误差而言，混合方法优于其他测试方法。

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