Pattern-based local linear regression models for short-term load forecasting

• Linear regression models for pattern-based short-term load forecasting are proposed.
• Forecasting time series with multiple seasonal cycles is simplified when using patterns.
• The local nature of the models leads to their simplification and accuracy improvement.
• Principal component and partial least-squares regressions gave best results in STLF.

In this paper univariate models for short-term load forecasting based on linear regression and patterns of daily cycles of load time series are proposed. The patterns used as input and output variables simplify the forecasting problem by filtering out the trend and seasonal variations of periods longer than the daily one. The nonstationarity in mean and variance is also eliminated. The simplified relationship between variables (patterns) is modeled locally in the neighborhood of the current input using linear regression. The load forecast is constructed from the forecasted output pattern and the current values of variables describing the load time series. The proposed stepwise and lasso regressions reduce the number of predictors to a few. In the principal components regression and partial least-squares regression only one predictor is used. This allows us to visualize the data and regression function. The performances of the proposed methods were compared with that of other models based on ARIMA, exponential smoothing, neural networks and Nadaraya–Watson estimator. Application examples confirm valuable properties of the proposed approaches and their high accuracy.

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