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Found 11 papers, 0 papers with code
Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time-Series Forecast
The proposed architecture significantly improves up to 25 percent for multi-task learning for power forecasts on the EuropeWindFarm and GermanSolarFarm dataset compared to the multi-layer perceptron approach.
Model Selection, Adaptation, and Combination for Transfer Learning in Wind and Photovoltaic Power Forecasts
Therefore, we adopt source models based on target data from different seasons and limit the amount of training data.
Synthetic Photovoltaic and Wind Power Forecasting Data
Since the synthetic time series are based exclusively on weather measurements, possible errors in the weather forecast are comparable to those in actual power data.
Quantile Surfaces -- Generalizing Quantile Regression to Multivariate Targets
In this article, we present a novel approach to multivariate probabilistic forecasting.
Emerging Relation Network and Task Embedding for Multi-Task Regression Problems
Results suggest that the ern is beneficial when tasks are only loosely related and the prediction problem is more non-linear.
Extended Coopetitive Soft Gating Ensemble
This article is about an extension of a recent ensemble method called Coopetitive Soft Gating Ensemble (CSGE) and its application on power forecasting as well as motion primitive forecasting of cyclists.
Generative Adversarial Networks for Operational Scenario Planning of Renewable Energy Farms: A Study on Wind and Photovoltaic
In these scenarios, operators examine temporal as well as spatial influences of different energy sources on the grid.
Transfer Learning in the Field of Renewable Energies -- A Transfer Learning Framework Providing Power Forecasts Throughout the Lifecycle of Wind Farms After Initial Connection to the Electrical Grid
In recent years, transfer learning gained particular interest in the field of vision and natural language processing.
Influences in Forecast Errors for Wind and Photovoltaic Power: A Study on Machine Learning Models
Despite the increasing importance of forecasts of renewable energy, current planning studies only address a general estimate of the forecast quality to be expected and selected forecast horizons.
Quantifying the Influences on Probabilistic Wind Power Forecasts
Therefore, we examine the potential influences with techniques from the field of sensitivity analysis on three different black-box models to obtain insights into differences and similarities of these probabilistic models.
Coopetitive Soft Gating Ensemble
In this article, we propose the Coopetititve Soft Gating Ensemble or CSGE for general machine learning tasks and interwoven systems.
