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首页> 外文会议>International Conference on Economics, Management Engineering and Education Technology >Application of Improved Particle Swarm Optimization Algorithm in Medium and Long Term power Load Combination Forecasting
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Application of Improved Particle Swarm Optimization Algorithm in Medium and Long Term power Load Combination Forecasting

机译：改进粒子群优化算法在中长期电力负荷组合预测中的应用

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摘要

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.
机译：有许多电力负荷预测方法，但每种方法都有其自身不准确的影响因素，并且单个方法的结果相对较大。中长期电力负荷预测对未来的当地规划的发展影响，因此预测的准确性更高。基于两个阶指数平滑，回归分析和灰色预测模型，构建了基于三种单一预测方法的综合预测模型。通过使用粒子群优化算法的惯性重量来确定权重，通过比较获得改进的组合预测的优点。

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