Abstract
With the introduction of smart metering infrastructure in the power system, the availability of real-time data for every node in a smart grid has become possible. This has led to the availability of big data in power system. The analysis of this huge set of data requires a different method of treatment. Machine learning-based tools are being used in this environment for load forecasting. But this huge data set requires appropriate pre-processing for bad data removal, missing data identification, normalization of largely varying datasets, etc. to enable the load forecaster to perform better. The present paper focuses on the analysis of different filters or pre-processors in the performance of multiple load forecasting methods in the case of smart grid. The paper uses a practical smart grid data to implement the same.
This work is supported by the Science & Engineering Research Board, a statutory body of Department of Science and Technology (DST), Government of India, under grants number ECR/2017/001027.
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Rai, S., De, M. (2020). Effect of Filtering in Big Data Analytics for Load Forecasting in Smart Grid. In: Bhattacharjee, A., Borgohain, S., Soni, B., Verma, G., Gao, XZ. (eds) Machine Learning, Image Processing, Network Security and Data Sciences. MIND 2020. Communications in Computer and Information Science, vol 1240. Springer, Singapore. https://doi.org/10.1007/978-981-15-6315-7_10
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DOI: https://doi.org/10.1007/978-981-15-6315-7_10
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