摘要
针对当前能耗预测时,普遍存在预测时间较长、拟合优度较低、成本开销较大等问题,提出基于人工鱼群算法的能耗预测方法.引入时间序列方法寻找出附近2个变量之间温度能耗密切程度,获取建筑温度能耗相关函数,得出建筑温度能耗季节关联性,采用人工鱼群算法对神经网络阈值进行优化,构建预测模型,以此完成能耗预测.实验结果表明,所提出方法预测时间较短,成本消耗较低,拟合优度较高.
The current temperature regulation energy consumption forecast of residential buildings in dense areas has long prediction time, low goodness of fit, and high cost. An energy consumption prediction method based on artificial fish swarm algorithm is proposed. The time series method is introduced to find the temperature energy consumption between two adjacent variables. The correlation function of building temperature and energy consumption and the seasonal correlation of building temperature and energy consumption were obtained. The artificial fish swarm algorithm was used to optimize the threshold of BP neural network, and the overall temperature energy consumption prediction model of the building was constructed. The temperature regulation energy consumption prediction of dense residential buildings was completed. The experimental results show that the prediction time required in the consumption prediction is shorter, the cost is lower, and the goodness of fit is higher than the current temperature regulation energy consumption forecast of residential buildings.
引文
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