Hierarchical Electricity Demand Forecasting by Exploring the Electricity Consumption Patterns

Yue Pang, Chaoyi Jin, Xiangdong Zhou, Naiwang Guo, Yong Zhang

Abstract

Accurate electricity demand forecasting is necessary to develop an efficient and sustainable power system. Total demand of the whole region can be disaggregated at different levels, thus producing a hierarchical structure. In the hierarchical demand forecasting, the prediction accuracy and aggregate consistency between levels are two important issues, however in the previous works the prediction accuracy is often affected by conducting the aggregate consistency. In this work, we propose a novel pattern-based hierarchical time series forecasting (PHF) method which consists of two aggregation stages. In the first aggregation stage, by exploring the electricity consuming patterns with clustering method, the bottom level electricity demand forecasting is improved, and in the second stage the region level aggregation is conducted to achieve the whole level forecasting. The experiments are conducted on the Energy Demand Research Project (EDRP) datasets, and the experimental results show that compared with the previous state-of-the-art methods, our method improves the prediction accuracy in all hierarchical levels with keeping aggregation consistency.

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Paper Citation


in Harvard Style

Pang Y., Jin C., Zhou X., Guo N. and Zhang Y. (2018). Hierarchical Electricity Demand Forecasting by Exploring the Electricity Consumption Patterns.In Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-276-9, pages 576-581. DOI: 10.5220/0006715005760581


in Bibtex Style

@conference{icpram18,
author={Yue Pang and Chaoyi Jin and Xiangdong Zhou and Naiwang Guo and Yong Zhang},
title={Hierarchical Electricity Demand Forecasting by Exploring the Electricity Consumption Patterns},
booktitle={Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2018},
pages={576-581},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006715005760581},
isbn={978-989-758-276-9},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Hierarchical Electricity Demand Forecasting by Exploring the Electricity Consumption Patterns
SN - 978-989-758-276-9
AU - Pang Y.
AU - Jin C.
AU - Zhou X.
AU - Guo N.
AU - Zhang Y.
PY - 2018
SP - 576
EP - 581
DO - 10.5220/0006715005760581