日種別・日射量を考慮した時刻別回帰型トレンド調整項付き需要モデリングによる電力ロードカーブ予測手法

摘要

This paper presents a daily load curve forecasting method using hourly regressions. Electric load varies significantly during the day. Many factors, such as weather conditions, hours and day-types, relate to the load behavior. We formulate a daily load curve in set of independent 24-h regression equations that split hourly load into four parts: weather related load (WE), weekday's day-types (DTW), non-weekday's day-types (DTH), and yearly trends (TR). We incorporate temperature, humidity and insolation into the WE part. Non-linear relationships of weather factors and loads are formulated by polynomial functions. Another approach, based on Gaussian functions, is also applied to the modeling for the weather and load relationships. In order to estimate regression coefficients properly, we should consider seasonal load change and estimate the coefficients with statistically enough amounts of data. The proposed method estimates the equations with data from both forecasting year and past years. TR compensates yearly load difference among the data. As TR representations, we propose 'additive trends model' and 'multiple trends model'. Experimental studies on the next day load forecasting are carried out with TEPCO system load. The results indicate effectiveness of (1) combination of 'polynomial function' and 'multiple trends model' and (2) 'Day-types' and 'Insolation'; in the next day load curve forecasting. Performance of less than 1% MAPE is also observed on the next day weekdays' peak load forecasting.%電力会社の中央給電指令所では供給予備力の確保と発電rn機の効率運用を目的に，当日や翌日の電力需要を毎日予測rnしている。電力需要は時刻，曜日，天候などに応じて変化すrnる複雑な予測村象であり，予測には高度な熟練と多岐にrnわたる膨大なデータ分析が要求される。