基于人工神经模糊和神经网络的长期电力需求预测

O. T. Altinoz, Erhan Mengusoglu
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引用次数: 0

摘要

供需均衡是电力生产企业和普通(家庭)用户确定电价的主要标准。这些公司必须确定未来的电力需求,以实现不间断的高效电力供应。电力需求受天气条件、经济运行过程、一年中的工作日和非工作日等因素的影响。因此,利用当前和历史数据预测电力需求对电力交易和生产企业来说是非常重要的。本研究提出一种基于神经网络模型的云端预测服务,用于土耳其长期电力需求预测。所提出的系统基于云的特性有助于持续训练,并随着时间的推移提高系统的预测能力。用神经网络和人工神经模糊推理系统对下一年的总电力需求进行了近似估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cloud-based long term electricity demand forecasting using artificial neuro-fuzzy and neural networks
The supply-demand equilibrium is the main criteria for determination of electricity pricing for both electrical power production companies and ordinary (household) users. The companies must be sure about future demands of electricity for uninterrupted efficient electrical supply. The demand of electricity is affected from weather conditions, process of economy, working and nonworking days of a year, etc. Therefore, forecasting demand by using current and historical data is very important for electricity trading and producing companies. In this study, a cloud-based forecasting service which is based on neural network model is proposed for long-term electricity demand forecasting of Turkey. Cloud based nature of the proposed system help continuous training and improved forecasting capability over time from the system. Following year overall electric demand is approximately estimated with neural network and artificial neuro-fuzzy inference systems.
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