Search Results for author:
Found 9 papers, 4 papers with code
Short-term Prediction and Filtering of Solar Power Using State-Space Gaussian Processes
Short-term forecasting of solar photovoltaic energy (PV) production is important for powerplant management.
Discovery methods for systematic analysis of causal molecular networks in modern omics datasets
With the increasing availability and size of multi-omics datasets, investigating the casual relationships between molecular phenotypes has become an important aspect of exploring underlying biology and genetics.
Interpreting wide-band neural activity using convolutional neural networks
Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings.
Short-Term Solar Irradiance Forecasting Using Calibrated Probabilistic Models
Advancing probabilistic solar forecasting methods is essential to supporting the integration of solar energy into the electricity grid.
Short-term prediction of photovoltaic power generation using Gaussian process regression
The present paper focuses on evaluating predictions of the energy generated by PV systems in the United Kingdom Gaussian process regression (GPR).
Neural NILM: Deep Neural Networks Applied to Energy Disaggregation
Energy disaggregation estimates appliance-by-appliance electricity consumption from a single meter that measures the whole home's electricity demand.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Demo Abstract: NILMTK v0.2: A Non-intrusive Load Monitoring Toolkit for Large Scale Data Sets
In this demonstration, we present an open source toolkit for evaluating non-intrusive load monitoring research; a field which aims to disaggregate a household's total electricity consumption into individual appliances.
Other Computer Science
NILMTK: An Open Source Toolkit for Non-intrusive Load Monitoring
We demonstrate the range of reproducible analyses which are made possible by our toolkit, including the analysis of six publicly available data sets and the evaluation of both benchmark disaggregation algorithms across such data sets.
Applications
Metadata for Energy Disaggregation
Energy disaggregation is the process of estimating the energy consumed by individual electrical appliances given only a time series of the whole-home power demand.
Databases H.3
