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Energies 2018, 11(6), 1561; https://doi.org/10.3390/en11061561

A Novel Hybrid Interval Prediction Approach Based on Modified Lower Upper Bound Estimation in Combination with Multi-Objective Salp Swarm Algorithm for Short-Term Load Forecasting

1
Faculty of Information Technology, Macau University of Science and Technology, Macau 999078, China
2
School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China
3
Gansu Meteorological Service Centre, Lanzhou 730020, China
*
Author to whom correspondence should be addressed.
Received: 25 May 2018 / Revised: 8 June 2018 / Accepted: 10 June 2018 / Published: 14 June 2018
(This article belongs to the Special Issue Short-Term Load Forecasting by Artificial Intelligent Technologies)
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Abstract

Effective and reliable load forecasting is an important basis for power system planning and operation decisions. Its forecasting accuracy directly affects the safety and economy of the operation of the power system. However, attaining the desired point forecasting accuracy has been regarded as a challenge because of the intrinsic complexity and instability of the power load. Considering the difficulties of accurate point forecasting, interval prediction is able to tolerate increased uncertainty and provide more information for practical operation decisions. In this study, a novel hybrid system for short-term load forecasting (STLF) is proposed by integrating a data preprocessing module, a multi-objective optimization module, and an interval prediction module. In this system, the training process is performed by maximizing the coverage probability and by minimizing the forecasting interval width at the same time. To verify the performance of the proposed hybrid system, half-hourly load data are set as illustrative cases and two experiments are carried out in four states with four quarters in Australia. The simulation results verified the superiority of the proposed technique and the effects of the submodules were analyzed by comparing the outcomes with those of benchmark models. Furthermore, it is proved that the proposed hybrid system is valuable in improving power grid management. View Full-Text
Keywords: short-term load forecasting; interval prediction; lower upper bound estimation; artificial intelligence; multi-objective optimization algorithm; data preprocessing short-term load forecasting; interval prediction; lower upper bound estimation; artificial intelligence; multi-objective optimization algorithm; data preprocessing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Wang, J.; Gao, Y.; Chen, X. A Novel Hybrid Interval Prediction Approach Based on Modified Lower Upper Bound Estimation in Combination with Multi-Objective Salp Swarm Algorithm for Short-Term Load Forecasting. Energies 2018, 11, 1561.

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