为提高光伏发电系统短期出力预测的精度,提出了一种和声搜索(Harmony Search,HS)算法与回声状态网络(Echo State Network,ESN)算法相结合的预测模型。该模型以光伏电站的历史发电量数据和气象数据为基础。首先通过相似日选择算法挑选出...为提高光伏发电系统短期出力预测的精度,提出了一种和声搜索(Harmony Search,HS)算法与回声状态网络(Echo State Network,ESN)算法相结合的预测模型。该模型以光伏电站的历史发电量数据和气象数据为基础。首先通过相似日选择算法挑选出预测日的相似日,将相似日的气象特征向量和预测日的气象特征向量的差值作为预测模型的输入变量;然后选择训练样本,并用和声搜索算法优化后的回声状态网络模型(HS-ESN)对样本进行训练和预测;最后以甘肃某光伏电站为例进行实例验证。实证分析表明,利用和声搜索算法优化回声状态网络预测模型的储备池参数可有效提高回声状态网络的预测精度,因此该模型具有较好的实用价值。展开更多
In this context, a novel structure was proposed for improving harmony search （HS） algorithm to solve the unit comment （UC） problem. The HS algorithm obtained optimal solution for defined objective function by impr...In this context, a novel structure was proposed for improving harmony search （HS） algorithm to solve the unit comment （UC） problem. The HS algorithm obtained optimal solution for defined objective function by improvising, updating and checking operators. In the proposed improved self-adaptive HS （SGHS） algorithm, two important control parameters were adjusted to reach better solution from the simple HS algorithm. The objective function of this study consisted of operation, start-up and shut-down costs. To confirm the effectiveness, the SGHS algorithm was tested on systems with 10, 20, 40 and 60 generating units, and the obtained results were compared with those of the simple HS algorithm and other related works.展开更多
Van Genuchten方程是最常用的土壤水分特征曲线方程,运用该方程的关键是4个参数的取值精度。为了精确地求解这些参数,引入和声搜索(HS)算法进行求解,提出一种基于全局信息的和声搜索优化计算方法——IGHS。IGHS算法具有如下特点:利用当...Van Genuchten方程是最常用的土壤水分特征曲线方程,运用该方程的关键是4个参数的取值精度。为了精确地求解这些参数,引入和声搜索(HS)算法进行求解,提出一种基于全局信息的和声搜索优化计算方法——IGHS。IGHS算法具有如下特点:利用当前和声记忆库中的全局最优解产生新解,改变了和声搜索算法新解的产生方式;通过对和声记忆库中当前最优解的扰动避免算法早熟,增强算法的全局搜索能力;IGHS算法结构简单,容易实现。实验结果表明IGHS算法求解Van Genuchten方程参数的精度与随机微粒群结果相似,但其收敛速快、计算量小,因此可以作为计算Van Genuchten方程参数的新方法。展开更多
This research provides academic and practical contributions. From a theoretical standpoint, a hybrid harmony search(HS)algorithm, namely the oppositional global-based HS(OGHS), is proposed for solving the multi-object...This research provides academic and practical contributions. From a theoretical standpoint, a hybrid harmony search(HS)algorithm, namely the oppositional global-based HS(OGHS), is proposed for solving the multi-objective flexible job-shop scheduling problems(MOFJSPs) to minimize makespan, total machine workload and critical machine workload. An initialization program embedded in opposition-based learning(OBL) is developed for enabling the individuals to scatter in a well-distributed manner in the initial harmony memory(HM). In addition, the recursive halving technique based on opposite number is employed for shrinking the neighbourhood space in the searching phase of the OGHS. From a practice-related standpoint, a type of dual vector code technique is introduced for allowing the OGHS algorithm to adapt the discrete nature of the MOFJSP. Two practical techniques, namely Pareto optimality and technique for order preference by similarity to an ideal solution(TOPSIS), are implemented for solving the MOFJSP.Furthermore, the algorithm performance is tested by using different strategies, including OBL and recursive halving, and the OGHS is compared with existing algorithms in the latest studies.Experimental results on representative examples validate the performance of the proposed algorithm for solving the MOFJSP.展开更多
文摘为提高光伏发电系统短期出力预测的精度,提出了一种和声搜索(Harmony Search,HS)算法与回声状态网络(Echo State Network,ESN)算法相结合的预测模型。该模型以光伏电站的历史发电量数据和气象数据为基础。首先通过相似日选择算法挑选出预测日的相似日,将相似日的气象特征向量和预测日的气象特征向量的差值作为预测模型的输入变量;然后选择训练样本,并用和声搜索算法优化后的回声状态网络模型(HS-ESN)对样本进行训练和预测;最后以甘肃某光伏电站为例进行实例验证。实证分析表明,利用和声搜索算法优化回声状态网络预测模型的储备池参数可有效提高回声状态网络的预测精度,因此该模型具有较好的实用价值。
文摘In this context, a novel structure was proposed for improving harmony search （HS） algorithm to solve the unit comment （UC） problem. The HS algorithm obtained optimal solution for defined objective function by improvising, updating and checking operators. In the proposed improved self-adaptive HS （SGHS） algorithm, two important control parameters were adjusted to reach better solution from the simple HS algorithm. The objective function of this study consisted of operation, start-up and shut-down costs. To confirm the effectiveness, the SGHS algorithm was tested on systems with 10, 20, 40 and 60 generating units, and the obtained results were compared with those of the simple HS algorithm and other related works.
基金supported by the National Key Research and Development Program of China(2016YFD0700605)the Fundamental Research Funds for the Central Universities(JZ2016HGBZ1035)the Anhui University Natural Science Research Project(KJ2017A891)
文摘This research provides academic and practical contributions. From a theoretical standpoint, a hybrid harmony search(HS)algorithm, namely the oppositional global-based HS(OGHS), is proposed for solving the multi-objective flexible job-shop scheduling problems(MOFJSPs) to minimize makespan, total machine workload and critical machine workload. An initialization program embedded in opposition-based learning(OBL) is developed for enabling the individuals to scatter in a well-distributed manner in the initial harmony memory(HM). In addition, the recursive halving technique based on opposite number is employed for shrinking the neighbourhood space in the searching phase of the OGHS. From a practice-related standpoint, a type of dual vector code technique is introduced for allowing the OGHS algorithm to adapt the discrete nature of the MOFJSP. Two practical techniques, namely Pareto optimality and technique for order preference by similarity to an ideal solution(TOPSIS), are implemented for solving the MOFJSP.Furthermore, the algorithm performance is tested by using different strategies, including OBL and recursive halving, and the OGHS is compared with existing algorithms in the latest studies.Experimental results on representative examples validate the performance of the proposed algorithm for solving the MOFJSP.
