A particle swarm optimization to identifying the ARMAX model for short-term load forecasting
A particle swarm optimization to identifying the ARMAX model for short-term load forecasting
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DOI：
10.1109/tpwrs.2005.846106
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发表时间：
2005-05-01
影响因子：
6.6
通讯作者：
Wang, ML
中科院分区：
文献类型：
--
作者：
Huang, CM;Huang, CJ;Wang, ML
提出了一种新的粒子群优化（PSO）算法，用于辨识带外生变量的自回归滑动平均（ARMAX）模型，并用于1天到1周的小时负荷预测。由于电力系统负荷固有的非线性特性，预测误差函数的曲面存在许多局部极小点。因此，基于梯度搜索的随机时间序列（STS）技术的解决方案可能会在局部最小点处停滞，从而导致模型不充分。通过模拟一个简化的社会系统，粒子群算法提供了收敛到一个复杂的误差曲面的全局极小点的能力。在不同类型的台电负荷数据上进行了测试，并与进化规划（EP）算法和传统的STS方法进行了比较。测试结果表明，该算法具有高质量的解，上级收敛特性，计算时间短。
In this paper, a new particle swarm optimization (PSO) approach to identifying the autoregressive moving average with exogenous variable (ARMAX) model for one-day to one-week ahead hourly load forecasts was proposed. Owing to the inherent nonlinear characteristics of power system loads, the surface of the forecasting error function possesses many local minimum points. Solutions of the gradient search-based stochastic time series (STS) technique may, therefore, stall at the local minimum points, which lead to an inadequate model. By simulating a simplified social system, the PSO algorithm offers the capability of converging toward the global minimum point of a complex error surface. The proposed PSO has been tested on the different types of Taiwan Power (Taipower) load data and compared with the evolutionary programming (EP) algorithm and the traditional STS method. Testing results indicate that the proposed PSO has high-quality solution, superior convergence characteristics, and shorter computation time.