Alamaniotis Miltiadis, Bargiotas Dimitrios, Tsoukalas Lefteri H
Applied Intelligent Systems Laboratory, School of Nuclear Engineering, Purdue University, 400 Central Dr., West Lafayette, IN 47907 USA.
Department of Electrical Engineering, Technological Institute of Stereas Elladas, 34400 Dimos Dirfion-Messapion, Psachna, Evia Greece.
Springerplus. 2016 Jan 20;5:58. doi: 10.1186/s40064-016-1665-z. eCollection 2016.
Integration of energy systems with information technologies has facilitated the realization of smart energy systems that utilize information to optimize system operation. To that end, crucial in optimizing energy system operation is the accurate, ahead-of-time forecasting of load demand. In particular, load forecasting allows planning of system expansion, and decision making for enhancing system safety and reliability. In this paper, the application of two types of kernel machines for medium term load forecasting (MTLF) is presented and their performance is recorded based on a set of historical electricity load demand data. The two kernel machine models and more specifically Gaussian process regression (GPR) and relevance vector regression (RVR) are utilized for making predictions over future load demand. Both models, i.e., GPR and RVR, are equipped with a Gaussian kernel and are tested on daily predictions for a 30-day-ahead horizon taken from the New England Area. Furthermore, their performance is compared to the ARMA(2,2) model with respect to mean average percentage error and squared correlation coefficient. Results demonstrate the superiority of RVR over the other forecasting models in performing MTLF.
能源系统与信息技术的整合推动了智能能源系统的实现，这种系统利用信息来优化系统运行。为此，精确、提前的负荷需求预测对于优化能源系统运行至关重要。特别是，负荷预测有助于系统扩展规划以及增强系统安全性和可靠性的决策制定。本文介绍了两种核机器在中期负荷预测（MTLF）中的应用，并基于一组历史电力负荷需求数据记录了它们的性能。这两种核机器模型，更具体地说是高斯过程回归（GPR）和相关向量回归（RVR），用于对未来负荷需求进行预测。GPR和RVR这两种模型均配备高斯核，并针对取自新英格兰地区提前30天的每日预测进行测试。此外，将它们的性能与ARMA(2,2)模型在平均百分比误差和平方相关系数方面进行了比较。结果表明，在进行中期负荷预测时，RVR优于其他预测模型。
