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- W3199104292 abstract "In order to forecast Short-Term Load Forecast (STLF) for individual electric customers and then intelligently and flexibly utilize renewable energy, a Big Data framework is proposed that collects the data from smart meters and weather forecasting, pre-processes and loads it into a NoSQL database that is capable to store and further process large volumes of heterogeneous data. Then, a Long Short-Term Memory (LSTM) recurrent neural network is designed and implemented to determine the load profiles and forecast the electricity consumption for residential community for the next 24 hours. The proposed framework is tested with a publicly available smart meter dataset of a residential community, of which LSTM’s performance is compared with two benchmark algorithms in terms of Root Mean Square Errors (RMSE) and Mean Absolute Percentage Errors (MAPE)." @default.
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- W3199104292 date "2020-07-01" @default.
- W3199104292 modified "2023-09-27" @default.
- W3199104292 title "A Big Data Framework for Short-Term Load Forecasting Using Heterogenous Data" @default.
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- W3199104292 doi "https://doi.org/10.1109/icvris51417.2020.00123" @default.
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