Optimal Short Term Power Load Forecasting Algorithm by Using Improved Artificial Intelligence Technique

机译：改进人工智能技术的短期电力负荷最优预测算法

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Electrical load forecasting plays a significant impact in terms of future power generation systems such as smart grid, power demand approximation, and better energy management system. Therefore, high accuracy is needed for different time horizons related to regulating, dispatch and scheduling of power system grid. However, it is difficult to do energy prediction with high precision because of influencing factors such as climate, social and seasonal factors. Artificial Intelligence (AI) and Support Vector Machine (SVM) are proved to be capable of handle complex systems and deployed worldwide in many applications due to its superiority on other techniques. The improved short term load forecasting algorithm has been introduced in this research to analyze, discuss and deal with the enhanced electrical power system. The related constraints, influential factors are given and the experimental results can be validated by the effective outcome.

机译：电力负荷预测对未来的发电系统（例如智能电网，电力需求估算和更好的能源管理系统）产生重大影响。因此，对于与电力系统电网的调节，调度和调度有关的不同时间范围，需要高精度。然而，由于诸如气候，社会和季节因素的影响因素，难以高精度地进行能量预测。事实证明，人工智能（AI）和支持向量机（SVM）具有处理复杂系统的能力，并且由于其在其他技术上的优越性而在全球范围内广泛应用。本研究引入了改进的短期负荷预测算法，以分析，讨论和处理增强型电力系统。给出了相关的约束条件，影响因素，并通过有效的结果验证了实验结果。

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《International Conference on Computer and Information Sciences》|2020年|1-4|共4页
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Waqar Waheed; Qingshan Xu;
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Load forecasting; Support vector machines; Forecasting; Environmental factors; Prediction algorithms; Real-time systems; Power systems;

机译：负荷预测;支持向量机;预测;环境因素;预测算法;实时系统;电力系统;

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Optimal Short Term Power Load Forecasting Algorithm by Using Improved Artificial Intelligence Technique

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