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A generalized ANN-based model for short-term load forecasting.

机译：一种基于ANN的广义模型，用于短期负荷预测。

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摘要

Short-term load forecasting (STLF) deals with forecasting of hourly system demand with a lead time ranging from one hour to 168 hours. The basic objective of the STLF is to provide for economic, reliable and secure operation of the power system.; This dissertation establishes a new approach to artificial neural network (ANN) based STLF. It first decomposes the prediction problem into representation and function approximation problems. The representation problem is solved using phase-space embedding which identifies time delay variables from load time series that are used in forecasting. The concept is inherently different from the methods used so far because it does not use correlated variables for forecasting. Temperature variables are included as well using identified embedding parameters. Function approximation problem is approached using ANN ensemble and active selection of a training set. Training set is selected based on predicted weather parameters for a prediction horizon. Selection is done applying the k-nearest neighbors technique in a temperature-based vector space. A novel approach of pilot set simulation is used to determine the number of hidden units for every forecast period. Ensemble consists of two ANNs which are trained and cross validated on complementary training sets. Final prediction is obtained by a simple average of two trained ANNs.; The described technique is used for predicting one week's load in four selected months in summer peaking and winter peaking US utilities. Mean absolute percent errors (MAPEs) for 24-hour lead time predictions are slightly greater than 2% for all months. For 120-hour lead time (weekday) predictions, MAPEs are around 2.3%. MAPEs for 48-hour lead time (weekend) predictions are around 2.5%. Maximal errors for these cases are around 7%. Predictions for one-hour lead time are slightly higher than 1% for all months, with maximal errors not exceeding 4.99%. Peak load MAPEs are 2.3% for both utilities. Maximal peak-load errors do not exceed 6%. The technique shows very good performance faced with sudden and large changes in weather. For changes in temperature larger than 20{dollar}spcirc{dollar}F for two consecutive days, forecasting error is smaller than 3.58%.
机译：短期负荷预测（STLF）处理每小时系统需求的预测，前置时间在1个小时到168个小时之间。 STLF的基本目标是为电力系统提供经济，可靠和安全的运行。本文为基于STLF的人工神经网络建立了一种新的方法。它首先将预测问题分解为表示和函数逼近问题。使用相空间嵌入解决了表示问题，该相空间嵌入从预测的负载时间序列中识别出时间延迟变量。该概念与到目前为止使用的方法本质上是不同的，因为它不使用相关变量进行预测。使用确定的嵌入参数还包括温度变量。使用ANN集成和主动选择训练集来解决函数逼近问题。基于预测地平线的预测天气参数选择训练集。选择是在基于温度的向量空间中应用k最近邻技术进行的。飞行员集模拟的一种新颖方法用于确定每个预测时段的隐藏单位数。 Ensemble由两个ANN组成，分别在补充训练集上进行训练和交叉验证。最终预测是通过两个训练过的人工神经网络的简单平均值获得的。所描述的技术用于预测美国公用事业夏季高峰期和冬季高峰期四个选定月份的一周负荷。所有月份的24小时交货时间预测的平均绝对百分比误差（MAPE）都略大于2％。对于120小时交货时间（工作日）的预测，MAPE约为2.3％。对于48小时交货时间（周末）的预测，MAPE约为2.5％。这些情况下的最大误差约为7％。所有月份一小时交货时间的预测都略高于1％，最大误差不超过4.99％。两种公用事业公司的峰值负载MAPE均为2.3％。最大峰值负载误差不超过6％。面对天气的突然和大变化，该技术显示出非常好的性能。对于连续两天温度变化超过20 {spF {dollar} F的预报误差小于3.58％。

著录项

  • 作者

    Drezga, Irislav.;

  • 作者单位

    Virginia Polytechnic Institute and State University.;

  • 授予单位 Virginia Polytechnic Institute and State University.;
  • 学科 Engineering Electronics and Electrical.; Artificial Intelligence.
  • 学位 Ph.D.
  • 年度 1996
  • 页码 192 p.
  • 总页数 192
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;人工智能理论;
  • 关键词

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