西安市日最大電力負(fù)荷率與氣象因子相關(guān)關(guān)系分析預(yù)報(bào)模型的建立

doi:10.3969/2019jms.0017

首頁 > 過刊瀏覽>2020年第40卷第3期 >421-426. DOI:10.3969/2019jms.0017

西安市日最大電力負(fù)荷率與氣象因子相關(guān)關(guān)系分析預(yù)報(bào)模型的建立
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                        10.3969/2019jms.0017
                    
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中圖分類號:P49
基金項(xiàng)目:陜西省氣象局科技創(chuàng)新基金計(jì)劃資助項(xiàng)目（2016Y-7）

Establishment of prediction model of relationship between daily maximum power load rate and meteorological factors in Xi'an

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基于西安市2010—2013年逐日最大電力負(fù)荷和同期的氣象資料，分析了日最大電力負(fù)荷的變化規(guī)律，利用最小二乘法，除去日最大負(fù)荷的節(jié)假日效應(yīng)和周末效應(yīng)后，將氣象負(fù)荷從日最大電力負(fù)荷中分離出來，建立西安氣象負(fù)荷率與氣溫、相對濕度、總云量、降水量、風(fēng)速的相關(guān)關(guān)系，并基于2010—2012年的11月—次年2月和6—8月的資料，分別采用逐步回歸、多元線性回歸和BP神經(jīng)網(wǎng)絡(luò)方法建立最大氣象負(fù)荷和主要?dú)庀笥绊懸蜃又g的預(yù)報(bào)模型，將2013年對應(yīng)時(shí)間的日最大氣象負(fù)荷率作為預(yù)報(bào)效果的獨(dú)立樣本檢驗(yàn)。結(jié)果顯示：2010—2013年西安的日最大電荷存在明顯的增長趨勢，且存在明顯的周末效應(yīng)和節(jié)假日效應(yīng)；氣溫是影響氣象負(fù)荷率的最顯著因子，引入溫濕指數(shù)（THI）的BP神經(jīng)網(wǎng)絡(luò)算法對氣象負(fù)荷率的擬合和預(yù)測效果最優(yōu)。

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Based on the daily maximum power load and meteorological data of Xi'an from 2010 to 2013, the variation law of daily maximum power load was analyzed, and the meteorological load was separated from daily maximum power load using the least square method after eliminating the holiday effect and weekend effect of daily maximum load. Then the correlation between meteorological load rate and temperature, relative humidity, total cloud, precipitation and wind speed in Xi'an was established. Based on the data of the period from November to February and the period from June to August from 2010 to 2012, the forecast models between the maximum meteorological load and the main meteorological factors were established by stepwise regression, multiple linear regression and BP neural network, respectively. The daily maximum meteorological load rate of the corresponding time in 2013 was taken as the independent sample test of the forecast effect. The results show that the daily maximum load in Xi'an from 2010 to 2013 has an obvious increasing trend year by year, and there are obvious weekend effect and holiday effect. Temperature is the most significant factor affecting the meteorological load rate, and the BP neural network algorithm with Temperature and Humidity Index (THI) is the best for fitting and forecasting the meteorological load rate.

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浩宇,管靚,張曦,沈姣姣,高紅燕.西安市日最大電力負(fù)荷率與氣象因子相關(guān)關(guān)系分析預(yù)報(bào)模型的建立.氣象科學(xué),2020,40(3):421-426 HAO Yu, GUAN Liang, ZHANG Xi, SHEN Jiaojiao, GAO Hongyan. Establishment of prediction model of relationship between daily maximum power load rate and meteorological factors in Xi'an. Journal of the Meteorological Sciences,2020,40(3):421-426

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收稿日期:2018-10-17
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