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A Single Scalable LSTM Model for Short-Term Forecasting of Massive Electricity Time Series
| Content Provider | MDPI |
|---|---|
| Author | Alonso, Andrés M. Nogales, Francisco J. Ruiz, Carlos |
| Copyright Year | 2020 |
| Description | Most electricity systems worldwide are deploying advanced metering infrastructures to collect relevant operational data. In particular, smart meters allow tracking electricity load consumption at a very disaggregated level and at high frequency rates. This data opens the possibility of developing new forecasting models with a potential positive impact on electricity systems. We present a general methodology that can process and forecast many smart-meter time series. Instead of using traditional and univariate approaches, we develop a single but complex recurrent neural-network model with long short-term memory that can capture individual consumption patterns and consumptions from different households. The resulting model can accurately predict future loads (short-term) of individual consumers, even if these were not included in the original training set. This entails a great potential for large-scale applications as once the single network is trained, accurate individual forecast for new consumers can be obtained at almost no computational cost. The proposed model is tested under a large set of numerical experiments by using a real-world dataset with thousands of disaggregated electricity consumption time series. Furthermore, we explore how geo-demographic segmentation of consumers may impact the forecasting accuracy of the model. |
| Starting Page | 5328 |
| e-ISSN | 19961073 |
| DOI | 10.3390/en13205328 |
| Journal | Energies |
| Issue Number | 20 |
| Volume Number | 13 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2020-10-13 |
| Access Restriction | Open |
| Subject Keyword | Energies Industrial Engineering Load Forecasting Disaggregated Time Series Neural Networks Smart Meters |
| Content Type | Text |
| Resource Type | Article |
