In the smart grid era, Smart Meter data is playing a vital role. The residential load forecasting is one of the major issue that uses the Smart Meter data. It is high volatile load data due to its nature of variability in load pattern. The load pattern of every customer varies with respect to certain factors like demographic information, environmental factors, time stamp, etc. Load pattern-based customer clustering, aggregated load value based customers classification are certain conventional method used for addressing the load forecasting. It is less likely to address forecasting issue using the conventional method. To overcome this downside, this research proposes the deep learning based load forecasting method that uses the aggregated smart meter dataset along with the demographic dataset. The performance of the deep neural network (DNN) is compared with shallow network that outperform with the error calculation.
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Deep Learning based Smart Meter Data Analytics for Electricity Load Prediction
Semantic Scholar · Engineering · 2019
Abstract
In the smart grid era, Smart Meter data is playing a vital role. The residential load forecasting is one of the major issue that uses the Smart Meter data. It is high volatile load data due to its nature of variability in load pattern. The load pattern of every customer varies with respect to certain factors like demographic information, environmental factors, time stamp, etc. Load pattern-based customer clustering, aggregated load value based customers classification are certain conventional method used for addressing the load forecasting. It is less likely to address forecasting issue using the conventional method. To overcome this downside, this research proposes the deep learning based load forecasting method that uses the aggregated smart meter dataset along with the demographic dataset. The performance of the deep neural network (DNN) is compared with shallow network that outperform with the error calculation.