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首页> 外文期刊>Energy Conversion & Management >A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey
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A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey

机译：基于粒子群优化和蚁群算法的混合动力预测土耳其能源需求

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This paper proposes a new hybrid method (HAP) for estimating energy demand of Turkey using Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). Proposed energy demand model (HAPE) is the first model which integrates two mentioned meta-heuristic techniques. While, PSO, developed for solving continuous optimization problems, is a population based stochastic technique; ACO, simulating behaviors between nest and food source of real ants, is generally used for discrete optimizations. Hybrid method based PSO and ACO is developed to estimate energy demand using gross domestic product (GDP), population, import and export. HAPE is developed in two forms which are linear (HAPEL) and quadratic (HAPEQ). The future energy demand is estimated under different scenarios. In order to show the accuracy of the algorithm, a comparison is made with ACO and PSO which are developed for the same problem. According to obtained results, relative estimation errors of the HAPE model are the lowest of them and quadratic form (HAPEQ) provides better-fit solutions due to fluctuations of the socio-economic indicators.
机译：本文提出了一种新的混合方法（HAP），使用粒子群优化（PSO）和蚁群优化（ACO）估计土耳其的能源需求。拟议的能源需求模型（HAPE）是第一个模型，该模型集成了两个提到的元启发式技术。 PSO是为解决连续优化问题而开发的，它是一种基于总体的随机技术。 ACO通常用于模拟真实蚂蚁的巢和食物源之间的行为，通常用于离散优化。已开发出基于PSO和ACO的混合方法，以利用国内生产总值（GDP），人口，进出口来估算能源需求。 HAPE以线性（HAPEL）和二次（HAPEQ）两种形式开发。在不同的情况下估计未来的能源需求。为了显示算法的准确性，对针对相同问题开发的ACO和PSO进行了比较。根据获得的结果，HAPE模型的相对估计误差是最低的，并且由于社会经济指标的波动，二次型（HAPEQ）提供了更好的拟合解决方案。

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