In this paper, a characteristic load decomposition (CLD)-based day-ahead load forecasting scheme is proposed for a mixed-use complex. The aggregated load of the complex is composed of the mixtures of different electricity usage patterns, and short-term load forecasting can be implemented by summing disaggregated sub-load predictions. However, tracing all usage patterns of sub-loads for prediction may be infeasible because of limited resources for measurement and analysis. To prevent this infeasibility, the proposed scheme focuses on effective decomposition using the sub-loads of typical characteristic load profiles and their representative pilot signals. Separate forecasts are obtained for the decomposed characteristic sub-loads using a hybrid scheme, which combines day-type conditioned linear prediction with long short-term memory regressions. Complex campus load data are considered to evaluate the proposed CLD-based hybrid forecasting. The evaluation results show that the proposed scheme outperforms conventional hybrid or similar-day-based forecasting approaches. Even when sub-load measurements are available only for a limited period, the CLD scheme can be applied for the extended training data through virtual disaggregations. INDEX TERMS Day-ahead load forecasting, time series analysis, long short-term memory, hybrid forecasting model, characteristic load decomposition, hierarchical load forecasting.

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