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https://doi.org/10.2139/ssrn.4656730

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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/0893-6080(91)90009-topenalex-extracted
Approximation capabilities of multilayer feedforward networks
inferredcites →https://doi.org/10.1016/j.apenergy.2017.08.141openalex-extracted
Electricity demand planning forecasts should consider climate non-stationarity to maintain reserve margins during heat waves
inferredcites →https://doi.org/10.1016/j.apenergy.2020.114949openalex-extracted
Non-intrusive load disaggregation solutions for very low-rate smart meter data
inferredcites →https://doi.org/10.1016/j.apenergy.2020.115564openalex-extracted
How to model European electricity load profiles using artificial neural networks
inferredcites →https://doi.org/10.1016/j.egyr.2021.08.195openalex-extracted
A data-driven method for unsupervised electricity consumption characterisation at the district level and beyond
inferredcites →https://doi.org/10.1016/j.enbuild.2019.109512openalex-extracted
Outdoor temperature sensitivity of electricity consumption for space heating and cooling: An application to the city of Milan, North of Italy
inferredcites →https://doi.org/10.1016/j.energy.2015.06.001openalex-extracted
The impact of the mean daily air temperature change on electricity consumption
inferredcites →https://doi.org/10.1016/j.enpol.2020.111445openalex-extracted
Impact of temperature on electricity demand: Evidence from Delhi and Indian states
inferredcites →https://doi.org/10.1016/s0140-9883(01)00082-2openalex-extracted
Temperature and seasonality influences on Spanish electricity load
inferredcites →https://doi.org/10.1109/access.2018.2871343openalex-extracted
Improving Event-Based Non-Intrusive Load Monitoring Using Graph Signal Processing
inferredcites →https://doi.org/10.1109/icassp.2016.7472104openalex-extracted
A novel DNN-HMM-based approach for extracting single loads from aggregate power signals
inferredcites →https://doi.org/10.1109/iecon.2012.6389367openalex-extracted
Event detection for Non Intrusive load monitoring
inferredcites →https://doi.org/10.1109/isgt.2019.8791578openalex-extracted
HVAC load Disaggregation using Low-resolution Smart Meter Data
inferredcites →https://doi.org/10.1109/mpot.2009.933498openalex-extracted
Cascading failures in power grids
inferredcites →https://doi.org/10.1109/pesgm40551.2019.8973732openalex-extracted
Non-Intrusive Load Monitoring using Electricity Smart Meter Data: A Deep Learning Approach
inferredcites →https://doi.org/10.1145/2821650.2821672openalex-extracted
Neural NILM
inferredcites →https://doi.org/10.1175/1520-0450(2001)040<1413:datael>2.0.co;2openalex-extracted
Daily Air Temperature and Electricity Load in Spain
inferredcites →https://doi.org/10.2307/1165017openalex-extracted
Critical Values for Spearman's Rank Order Correlation
inferredcites →https://doi.org/10.3102/10769986014003245openalex-extracted
Critical Values for Spearman's Rank Order Correlation
inferredcites →https://doi.org/10.3390/atmos8110209openalex-extracted
Impact of Cold Waves and Heat Waves on the Energy Production Sector
inferredcites →https://doi.org/10.48550/arxiv.1703.00785openalex-extracted
A Survey on Non-Intrusive Load Monitoring Methodies and Techniques for Energy Disaggregation Problem
inferredcites →https://doi.org/10.48550/arxiv.1912.01703openalex-extracted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
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