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Decision Procedure for Efficient Data Acquisition for Distinct Energy Prognosis Models

机译:关于不同能量预测模型的有效数据采集的决策程序

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The current challenges for enterprises are heavily linked to resource and energy efficiency, a result of political guidelines and the overall consciousness for environmental issues in society. Thus, the impact of energy efficiency on factory planning and production planning is of undeniable importance. Adequate consumption information utilising various depiction methods has become a necessity in this respect. Yet, only few works provide information on the efficiency of energy data acquisition methods. This paper proposes a methodological framework for defining an efficient procedure for energy data acquisition in accordance with the depiction or prognosis method. A preliminary literature review indicates the variety of methods for energy data depiction and prognosis. The classification of these methods regarding planning state and planning levels leads to the estimation of relevant input data. Based on the fundamental investigation of necessary input data the decision approach is developed with a general heuristic decision model and incorporates the Analytic Hierarchy Process for quantification and solution of the overall decision problem. Therefore the integration of multiple decision criteria enables the consideration of different quantitative and qualitative influences. As a result the whole approach supports the collaborative identification of means for energy data acquisition and is applicable in several circumstances where energy data needs to be gathered efficiently. The decision procedures exemplary application has shown the general proficiency of the approach and that excessive data acquisition which contradicts efficiency is avoidable.
机译:目前对企业的挑战与资源和能源效率大量相关,政治指导方针以及社会环境问题的整体意识。因此,能源效率对工厂规划和生产规划的影响是不可否认的重要性。利用各种描述方法的充分消费信息已成为这方面的必要性。然而,只有很少的作品提供了有关能源数据采集方法效率的信息。本文提出了一种方法框架,用于根据描述或预后方法定义能量数据采集的有效过程。初步文献综述表明了能量数据描述和预后的各种方法。关于规划状态和规划水平的这些方法的分类导致相关输入数据的估计。基于必要输入数据的基本调查,决策方法是通过通用启发式决策模型制定的,并融入了对整体决策问题的量化和解决方案的分析层次过程。因此,多个决策标准的集成使得能够考虑不同的定量和定性影响。结果,整个方法支持能量数据采集的手段的协作识别,并且适用于需要有效地收集能量数据的若干情况。决策程序示范申请表明了该方法的一般熟练程度,并且过多的数据采集矛盾效率是可避免的。

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