Comparing Ensemble-Based Forecasting Methods for Smart-Metering Data
This work provides a preliminary study on applying state-of-the-art time-series forecasting methods to electrical energy consumption data recorded by smart metering equipment. We compare a custom-build commercial baseline method to modern ensemble-based methods from statistical time-series analysis and to a modern commercial GP system. Our preliminary results indicate that that modern ensemble-based methods, as well as GP, are an attractive alternative to custom-built approaches for electrical energy consumption forecasting.
KeywordsRoot Mean Square Error Genetic Programming ARIMA Model Electrical Energy Consumption Symbolic Regression
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