Combining Machine Learning with Visual Analytics for Explainable Forecasting of Energy Demand in Prosumer Scenarios
Authors:
- Ana I. GrimaldoAGAna I. Grimaldo
- Jasminko NovakJNJasminko Novak
Open Access
- 1 January 2020
- journal article
- research article
- Published by Elsevier in Procedia Computer Science
- Vol. 175, 525-532
Abstract
No abstract availableKeywords
Funding Information
- Bundesministerium für Forschung, Technologie und Raumfahrt
This publication has 16 references indexed in Scilit:
- Explainable machine learning in deployment2020
- Abandoning the concept of renewable energyEnergy Policy, 2019
- Nonlinear autoregressive and random forest approaches to forecasting electricity load for utility energy management systemsSustainable Cities and Society, 2019
- Ensuring usability of future smart energy control room systemsEnergy Informatics, 2018
- Explainable software analytics2018
- Forecasting methods in energy planning modelsRenewable and Sustainable Energy Reviews, 2018
- Towards better analysis of machine learning models: A visual analytics perspectiveVisual Informatics, 2017
- Structured, physically inspired (gray box) models versus black box modeling for forecasting the output power of photovoltaic plantsEnergy, 2017
- Effective or ephemeral? The role of energy information dashboards in changing occupant energy behaviorsEnergy Research & Social Science, 2016
- Electricity market design for the prosumer eraNature Energy, 2016