Application of Improved Particle Swarm Optimization Algorithm in Medium and Long Term power Load Combination Forecasting

DOI

10.2991/icemeet-16.2017.6How to use a DOI?

Keywords

Power load forecasting; Combination forecasting; Particle swarm optimization; Inertia weight

Abstract

There are many methods of power load forecasting, but each method has its own inaccurate influence factors, and the result of the single method is relatively large. Medium and long term power load forecasting influence the development of local planning in the future, so the accuracy of prediction is higher. Based on the two order exponential smoothing, regression analysis and grey prediction model, the comprehensive prediction model based on the three single forecasting methods is built. By using the inertia weight of the particle swarm optimization algorithm to determine the weights, the advantage of the improved combination forecast is obtained by comparison.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Shuguo Zhang
AU  - Yang Su
PY  - 2017/01
DA  - 2017/01
TI  - Application of Improved Particle Swarm Optimization Algorithm in Medium and Long Term power Load Combination Forecasting
BT  - Proceedings of the 2016 2nd International Conference on Economics, Management Engineering and Education Technology (ICEMEET 2016)
PB  - Atlantis Press
SP  - 32
EP  - 35
SN  - 2352-5398
UR  - https://doi.org/10.2991/icemeet-16.2017.6
DO  - 10.2991/icemeet-16.2017.6
ID  - Zhang2017/01
ER  -