An Inherently Updatable On-line Load Forecasting Technique

短期负荷预测是一项重要而复杂的任务， 对技术和系统的深入了解。 许多 基于统计和专家系统的技术产生相当准确的 预报. 然而，大多数统计技术都是计算性的， 因为这些方法需要随着变化的条件而更新。 专家系统更能适应不断变化的条件， 并不总是容易表达现有的专业知识（知识）， 明确的数量条款，往往导致不一致的规则。 本研究的目的是开发一种新的负荷预测方法 一种技术，它结合了两个专家系统的能力 方法和统计技术，但避免了其缺点。 专家系统的方法是采用利用现有的机构， 形成最相关的历史数据子集的知识 到预测点。 优先级向量方法（在 建议，适用于定量表达任何定性因素 参与了这个过程。 这种方法是根本不同的 因为我们创造了历史数据的子集 根据预报点的情况，而不是 分析整个历史数据集的传统方法。 此外，在我们的技术中，更新功能变得自动化 由于数据的子集被连续地重构， 重新确定关系以匹配每个预测点。 概述了该技术最简单形式的发展， 并以周负荷预测为例进行了应用。 我们有 确定并简要讨论了需要进一步开展工作的领域， 使模型复杂化以获得更好的性能，并扩展它 进行区间预测。

Short term load forecasting is an important and complex task requiring in-depth understanding of the technique and the system. Many statistical and expert system based techniques produce fairly accurate forecast. However, most statistical techniques are computationally burdensome as these methods need updating with changing conditions. Expert systems are more adaptable to changing conditions, but it is not always easy to express the available expertise (knowledge) in clear quantitative terms, often leading to inconsistent rules. The objective of this research is to develop a new load forecasting technique which combines the capabilities of both the expert systems approach and the statistical techniques, but avoids their drawbacks. Expert systems approach is adopted to utilize the available body of knowledge to form a subset of historical data which are most pertinent to the forecast point. The priority vector method, explained in the proposal, is applied to quantitatively express any qualitative factors involved in the process. This approach is fundamentally different from the others, because we create subsets of historical data according to the conditions at the forecast point, instead of the traditional approach of analyzing the whole historical data set. Further, the updating function becomes automatic in our technique since the subset of data is continually reconstructed and the relationships redetermined to match each forecast point. The development of the technique in its simplest form is outlined and the application is demonstrated in weekly load forecast. We have identified and briefly discussed the areas which need further work to make the model sophisticated for better performance, and to extend it to perform interval forecasting.