This paper examines models based on Gaussian Process (GP) priors for electrical load forecasting. This methodology is seen to encompass a number of popular forecasting methods, such as Basic Structural Models (BSMs) and Seasonal Auto-Regressive Intergrated (SARI) as special cases. The GP forecasting models are shown to have some desirable properties and their performance is examined on weekly and yearly Irish load data
The emerging need for risk-aware operational decisions on power systems calls for the development of...
We present an electricity demand forecasting algorithm based on Gaussian processes. By introducing a...
[EN] The method described in this document makes it possible to use the techniques usually applied t...
This paper examines models based on Gaussian Process (GP) priors for electrical load forecasting. Th...
This paper presents a Gaussian process model-based short-term electric load forecasting. The Gaussia...
[[abstract]]This research used a hard-cut iterative training algorithm to improve a Gaussian process...
We provide a comprehensive framework for forecasting five minute load using Gaussian processes with ...
AbstractShort Term Load Forecasting (STLF) is essential for planning the day-to-day operation of an ...
Rainer Göb, Kristina Lurz and Antonio Pievatolo (hereinafter GLP) address a very important issue in ...
Growing perception of diverse generation resources and demand response operation of power system wit...
For participants in the energy industry, it is vital to have access to reliable forecasts of future ...
Accurate electricity load forecasting is of great importance in deregulated electricity markets. Mar...
Smart Grids become the next generation of environmentally beneficial and long-lasting electrical inf...
For the day-ahead density forecasting of electricity load, this paper proposes the combination of th...
Electricity forecasting is an essential component of smart grid, which has attracted increasing acad...
The emerging need for risk-aware operational decisions on power systems calls for the development of...
We present an electricity demand forecasting algorithm based on Gaussian processes. By introducing a...
[EN] The method described in this document makes it possible to use the techniques usually applied t...
This paper examines models based on Gaussian Process (GP) priors for electrical load forecasting. Th...
This paper presents a Gaussian process model-based short-term electric load forecasting. The Gaussia...
[[abstract]]This research used a hard-cut iterative training algorithm to improve a Gaussian process...
We provide a comprehensive framework for forecasting five minute load using Gaussian processes with ...
AbstractShort Term Load Forecasting (STLF) is essential for planning the day-to-day operation of an ...
Rainer Göb, Kristina Lurz and Antonio Pievatolo (hereinafter GLP) address a very important issue in ...
Growing perception of diverse generation resources and demand response operation of power system wit...
For participants in the energy industry, it is vital to have access to reliable forecasts of future ...
Accurate electricity load forecasting is of great importance in deregulated electricity markets. Mar...
Smart Grids become the next generation of environmentally beneficial and long-lasting electrical inf...
For the day-ahead density forecasting of electricity load, this paper proposes the combination of th...
Electricity forecasting is an essential component of smart grid, which has attracted increasing acad...
The emerging need for risk-aware operational decisions on power systems calls for the development of...
We present an electricity demand forecasting algorithm based on Gaussian processes. By introducing a...
[EN] The method described in this document makes it possible to use the techniques usually applied t...