Journal
ENERGIES
Volume 13, Issue 20, Pages -Publisher
MDPI
DOI: 10.3390/en13205328
Keywords
load forecasting; disaggregated time series; neural networks; smart meters
Categories
Funding
- Spanish government [MTM2017-88979-P, PID2019-108311GB-I00/AEI/10.13039/501100011033]
- Fundacion Iberdrola through Ayudas a la Investigacion en Energia y Medio Ambiente 2018
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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.
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