Load Forecasting Method based on Rough Set and Information Fusion Theory
Lifeng Chen, Yuechun Jiang, Fei He, Jie Zhang, Xuqiong Yang
Available Online August 2016.
- https://doi.org/10.2991/cset-16.2016.65How to use a DOI?
- Power load prediction, rough set method, data mining, information fusion
- With the requirements of the power load predicting accuracy becoming higher and higher, and the actual power load is also a complex nonlinear system limited by various uncertain factors. Given only considering the influence of a single model on the load forecasting, the predicting accuracy is absolutely not high under the comprehensive effects of multiple factors. Ensuring to make the electrical model satisfy the requirements, there is access to improving the accuracy of load predicting results through analysis of the relationship between load and multiple factors in power load forecasting. This paper combines the rough set theory with information fusion, firstly a decision table of attributes is established on the basis of historical datum, for the purpose of simplifying the attribute index in data mining to obtain important information. Following step is to rank the importance of the factors influencing the load according to the degree of effect, with grouping these sorted factors into attributes combination and using the same kind of load forecasting method to predict each combination's own results, and finally all groups of the predicting results are integrated by data fusion, where then final predicting result can be obtained. The results of the algorithmic example show that groups of predicting results integrated based on information fusion theory are better than the results got through any casual single attribute combination. And fusion results are a combined action of a variety of related factors, enhancing the favorable trend, where at the same time it has reduced the uncertainty brought by multiple factor comprehensive effect. Based on information fusion the predicting results are helpful to reduce the prediction error, which is feasible and effective.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Lifeng Chen AU - Yuechun Jiang AU - Fei He AU - Jie Zhang AU - Xuqiong Yang PY - 2016/08 DA - 2016/08 TI - Load Forecasting Method based on Rough Set and Information Fusion Theory BT - 2016 International Conference on Computer Science and Electronic Technology PB - Atlantis Press SN - 2352-538X UR - https://doi.org/10.2991/cset-16.2016.65 DO - https://doi.org/10.2991/cset-16.2016.65 ID - Chen2016/08 ER -