Abstract
Load Forecasting plays a key role in the efficient operation of the building energy management systems. In this work, a framework is proposed for effective scalable implementation of long-term (month and quarter ahead) building load forecasting. It comprises of techniques to deal with outliers and missing values, dynamic input feature selection as well as a hybrid algorithm combining direct and recursive strategies for forecasting. The solution is successfully validated using the real consumption data of six office buildings and further the average accuracies of 92–95% and 88–92% for month and quarter ahead respectively, corroborates its usefulness.
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Thokala, N.K., Spoorthy, S., Chandra, M.G. (2019). A Scalable Long-Horizon Forecasting of Building Electricity Consumption. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11506. Springer, Cham. https://doi.org/10.1007/978-3-030-20521-8_15
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DOI: https://doi.org/10.1007/978-3-030-20521-8_15
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