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Found 17 papers, 5 papers with code
Forecasting Electric Vehicle Charging Station Occupancy: Smarter Mobility Data Challenge
To fill that gap, the Smarter Mobility Data Challenge has focused on the development of forecasting models to predict EV charging station occupancy.
Adaptive Probabilistic Forecasting of Electricity (Net-)Load
The proliferation of local generation, demand response, and electrification of heat and transport are changing the fundamental drivers of electricity load and increasing the complexity of load modelling and forecasting.
Adaptive Conformal Predictions for Time Series
While recent works tackled this issue, we argue that Adaptive Conformal Inference (ACI, Gibbs and Cand{\`e}s, 2021), developed for distribution-shift time series, is a good procedure for time series with general dependency.
Daily peak electrical load forecasting with a multi-resolution approach
In the context of smart grids and load balancing, daily peak load forecasting has become a critical activity for stakeholders of the energy industry.
Hierarchical transfer learning with applications for electricity load forecasting
The recent abundance of data on electricity consumption at different scales opens new challenges and highlights the need for new techniques to leverage information present at finer scales in order to improve forecasts at wider scales.
State-Space Models Win the IEEE DataPort Competition on Post-covid Day-ahead Electricity Load Forecasting
On the one hand, purely time-series models such as autoregressives are adaptive in essence but fail to capture dependence to exogenous variables.
Transfer Learning for Linear Regression: a Statistical Test of Gain
Transfer learning, also referred as knowledge transfer, aims at reusing knowledge from a source dataset to a similar target one.
Adaptive Methods for Short-Term Electricity Load Forecasting During COVID-19 Lockdown in France
One of the consequences of this policy is a significant change in electricity consumption patterns.
Probabilistic Forecasting for Daily Electricity Loads and Quantiles for Curve-to-Curve Regression
When applying to one day ahead forecasting for the French daily electricity load curves, PPC outperform several state-of-the-art predictive methods in terms of forecasting accuracy, coverage rate and average length of the predictive bands.
Methodology Applications
Additive stacking for disaggregate electricity demand forecasting
We focus on forecasting demand at the individual household level, which is more challenging than forecasting aggregate demand, due to the lower signal-to-noise ratio and to the heterogeneity of consumption patterns across households.
Kalman Recursions Aggregated Online
In this article, we aim at improving the prediction of expert aggregation by using the underlying properties of the models that provide expert predictions.
Textual Data for Time Series Forecasting
While ubiquitous, textual sources of information such as company reports, social media posts, etc.
Target Tracking for Contextual Bandits: Application to Demand Side Management
We propose a contextual-bandit approach for demand side management by offering price incentives.
Nonnegative matrix factorization with side information for time series recovery and prediction
Motivated by the reconstruction and the prediction of electricity consumption, we extend Nonnegative Matrix Factorization~(NMF) to take into account side information (column or row features).
Nonnegative Matrix Factorization for Time Series Recovery From a Few Temporal Aggregates
Motivated by electricity consumption reconstitution, we propose a new matrix recovery method using nonnegative matrix factorization (NMF).
Recovering Multiple Nonnegative Time Series From a Few Temporal Aggregates
Motivated by electricity consumption metering, we extend existing nonnegative matrix factorization (NMF) algorithms to use linear measurements as observations, instead of matrix entries.
Adaptive Learning of Smoothing Functions: Application to Electricity Load Forecasting
In order to quickly track changes in the model and put more weight on recent data, the RLS filter uses a forgetting factor which exponentially weights down observations by the order of their arrival.
