Combining Machine Learning with Visual Analytics for Explainable Forecasting of Energy Demand in Prosumer Scenarios-Reference-Cited by-同舟云学术

Combining Machine Learning with Visual Analytics for Explainable Forecasting of Energy Demand in Prosumer Scenarios

Published:2020 Issue: Volume:175 Page:525-532
ISSN:1877-0509
Container-title:Procedia Computer Science
language:en
Short-container-title:Procedia Computer Science

Author:

Grimaldo Ana I.,Novak Jasminko

Publisher

Elsevier BV

Subject

General Engineering

Reference19 articles.

1. Electricity market design for the prosumer era;Parag;Nat. Energy,2016

2. Nonlinear autoregressive and random forest approaches to forecasting electricity load for utility energy management systems;Ahmad;Sustain. Cities Soc.,2019

3. A composite k-nearest neighbor model for day-ahead load forecasting with limited temperature forecasts;Zhang;IEEE Power Energy Soc. Gen. Meet.,2016

4. Effective or ephemeral ? The role of energy information dashboards in changing occupant energy behaviors;Timm;Energy Res. Soc. Sci.,2016

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