Logo Lanfrica

Uncovering Hidden Demand Flexibility Using Non-Intrusive Load Monitoring (NILM): A Case for Southern Africa—Preliminary Results for Erongo RED, Namibia

Domain:

environment and energy
Creator:
RacAndLinVla
Publisher:
Spr
Host:

Licenses

https://www.springernature.com/gp/researchers/text-and-data-mininghttps://www.springernature.com/gp/researchers/text-and-data-mining

Similar

Uncovering hidden states in African lion movement data using hidden Markov models

Context Direct observations of animals are the most reliable way to define the

Machine Learning Technique for the Prediction of Short-term Load Demand: A Case Study

The purpose of this paper is to present a machine-learning approach for forecasting short-term load

The human remains from Leopard Cave (Erongo, Namibia): New Mortuary Practices in Southern Africa more than 6,000 years ago?

International audience

Weather uncertainty and demand for information in technology adoption: Case of Namibia

Archaeological research in the Erongo mountains, Namibia: a Franco-Namibian cooperation.

International audience

Impact of a borderless sample transport network for scaling up viral load monitoring: results of a geospatial optimization model for Zambia

Abstract Introduction The World Health Organization recommends viral load (VL) monitoring at six