能源领域混合高级技术预测

English | PDF | 2018 | 252 Pages | ISBN : 3038972908 | 104.83 MB

Accurate forecasting performance in the energy sector is a primary factor in the modern restructured power market, accomplished by any novel advanced hybrid techniques. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated by factors such as seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. o comprehensively address this issue, it is insufficient to concentrate only on simply hybridizing evolutionary algorithms with each other, or on hybridizing evolutionary algorithms with chaotic mapping, quantum computing, recurrent and seasonal mechanisms, and fuzzy inference theory in order to determine suitable parameters for an existing model. It is necessary to also consider hybridizing or combining two or more existing models (e.g., neuro-fuzzy model, BPNN-fuzzy model, seasonal support vector regression-chaotic quantum particle swarm optimization (SSVR-CQPSO), etc.). These advanced novel hybrid techniques can provide more satisfactory energy forecasting performances.

This book aimed to attract researchers with an interest in the research areas described above. Specifically, we were interested in contributions towards recent developments, i.e., hybridizing or combining any advanced techniques in energy forecasting, with the superior capabilities over the traditional forecasting approaches, with the ability to overcome some embedded drawbacks, and with the very superiority to achieve significant improved forecasting accuracy.

准确的能源领域预测性能是现代重组电力市场中的一个关键因素，这需要采用任何新型先进的混合技术来实现。特别是在大数据时代，预测模型总是基于复杂的函数组合，并且能源数据经常受到季节性、周期性、波动性和动态非线性的等因素影响。 要全面解决这一问题，仅仅将进化算法彼此之间进行简单的混合，或将其与混沌映射、量子计算、递归和季节机制以及模糊推理理论相结合来确定现有模型的合适参数是不够的。还需要考虑将现有的两个或多个模型进行混合或组合（例如神经-模糊模型、BPNN-模糊模型、季节支持向量回归-混沌量子粒子群优化（SSVR-CQPSO）等）。这些先进的新型混合技术可以提供更满意的能源预测性能。 这本书旨在吸引对上述研究领域感兴趣的学者。具体来说，我们关注的是关于结合任何先进的预测技术的贡献，这些技术在传统预测方法上具有更强的能力，能够在嵌入式缺点中克服某些缺点，并显著提高预测精度的改进发展。

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