eprintid: 524
rev_number: 11
eprint_status: archive
userid: 7
dir: disk0/00/00/05/24
datestamp: 2012-01-30 16:31:41
lastmod: 2015-05-29 20:08:50
status_changed: 2012-01-30 16:31:41
type: report
metadata_visibility: show
item_issues_count: 0
creators_name: Hao, Z.
creators_name: Shao, D.
creators_name: Lu, L.
title: Power load forecasting
ispublished: pub
subjects: utilities
studygroups: chinese2006
full_text_status: public
abstract: For the electric power factory, the power load forecasting problem, including load forecasting and consumption predicting, is crucial to work planning. According to the predicting time, it can be divided into long-term forecasting, mid-term forecasting, short-term forecasting and ultra-short-term forecasting. The long-term and mid-term forecasting are mainly used for macro control, and their forecasting time arrange are from one year to ten years and from one month to twelve months respectively. The short-term forecasting which prediction time is from one day to seven days is used in generators macroeconomic control, power exchange plan and some other areas. Predicting the situation in next 24 hours is named as the ultra-short-term forecasting which is used for failure prediction, emergency treatment and frequency control. In general, the forecast accuracy is different for different prediction time. The longer is the time, the lower accurate is the prediction.

As the unique power supplier in Huizhou (China), Huizhou Electric Power wants to know the solution to the problems: 1. Prediction of the total electrical consumption and the peak load of the city in 2006 based on the economy development and the feature of the city. 2. Monthly prediction of the consumption and peak load in 2006. 3. Daily prediction of the consumption and peak load from July 10th to 16th in 2006. 4. Prediction of the load every 15 minutes of July 10th. 5. Real-time forecasting which means to amend the existing load prediction for next 15 minute.
date: 2006
citation:   Hao, Z. and Shao, D. and Lu, L.  (2006) Power load forecasting.  [Study Group Report]     
document_url: http://miis.maths.ox.ac.uk/miis/524/1/Power-load-forecasting.pdf