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Developing an effective interval-valued time series (ITS) forecasting scheme for electric power generation is an important issue for energy operators and governments when making energy strategic decisions. The existing studies for ITS forecasting only consider basic descriptive information such as center, radius, upper and lower bounds, and overlooks the distribution information within the data interval. In this study, an interval-valued time series forecasting scheme based on probability distribution information features of interval-valued data with machine learning algorithms is proposed to enhance electric power generation forecasting. In the proposed scheme, the central tendency features and dispersion features from the interval-valued data are designed as integrated features sets (IFS) and used as predictor variables. Three methods including supper vector regression and extreme learning machine and multivariate adaptive regression splines based on the IFS are utilized to develop ITS forecasting models. The daily time series of the metered generation from the Australian Energy Market Operator is used to illustrate the proposed scheme. Empirical results show that the proposed ITS forecasting schemes with IFS outperform the eight benchmark models and thus validate that the proposed scheme is an effective alternative for interval-valued electric power generation forecasting.

An Interval-Valued Time Series Forecasting Scheme With Probability Distribution Features for Electric Power Generation Prediction