Energy forecasting based on predictive data mining techniques in smart energy grids

Energy Informatics

Ekanki Sharma¹ 

[1] Alpen-Adria-Universität Klagenfurt, Universitätsstraße 65-67, Klagenfurt, Austria

关键词: Energy forecasting;    Weather-free data;    Data-driven model;    Anomaly detection;   

DOI  :  10.1186/s42162-018-0048-9

学科分类：计算机网络和通讯

来源: Springer

【 摘 要 】

Energy forecasting is a technique to predict future energy needs to achieve demand and supply equilibrium. In this paper we aim to assess the performance of a forecasting model which is a weather-free model created using a database containing relevant information about past produced power data and data mining techniques. The idea of using a weather-free data-driven model is first to alleviate the dependence on weather data which, in some scenarios is difficult to obtain and second to reduce the computational effort. In this work, we aim first to evaluate the interplay between anomaly detection techniques and forecasting model accuracy. Secondly we will determine out of the three defined performance metrics, which one is the best for this particular application.

【 授权许可】

CC BY