نتایج جستجو برای: multiple step ahead forecasting

تعداد نتایج: 1058493  

Journal: :CoRR 2017
Tinghui Ouyang Yusen He Huajin Li Zhiyu Sun Stephen Baek

The scheduling and operation of power system becomes prominently complex and uncertain, especially with the penetration of distributed power. Load forecasting matters to the effective operation of power system. This paper proposes a novel deep learning framework to forecast the short-term grid load. First, the load data is processed by Box-Cox transformation, and two parameters (electricity pri...

Journal: :Neurocomputing 2016
Mashud Rana Irena Koprinska

Electricity load forecasting is a key task in the planning and operation of power systems and electricity markets, and its importance increases with the advent of smart grids. In this paper, we present AWNN, a new approach for very short-term load forecasting. AWNN decomposes the complex electricity load data into components with different frequencies that are predicted separately. It uses an a...

2012
Anthony Florita Kirsten Orwig Debra Lew Michael Milligan

The introduction of large amounts of variable and uncertain power sources, such as wind power, into the electricity grid presents a number of challenges for system operations. One issue involves the uncertainty associated with scheduling power that wind will supply in future timeframes. However, this is not an entirely new challenge; load is also variable and uncertain, and is strongly influenc...

2011
Christian Brownlees Robert Engle Bryan Kelly

We present a volatility forecasting comparative study within the autoregressive conditional heteroskedasticity (ARCH) class of models. Our goal is to identify successful predictive models over multiple horizons and to investigate how predictive ability is influenced by choices for estimation window length, innovation distribution, and frequency of parameter reestimation. Test assets include a r...

Journal: :CoRR 2007
Thomas Sandholm

We study the predictive power of autoregressive moving average models when forecasting demand in two shared computational networks, PlanetLab and Tycoon. Demand in these networks is very volatile, and predictive techniques to plan usage in advance can improve the performance obtained drastically. Our key finding is that a random walk predictor performs best for one-step-ahead forecasts, whereas...

1999
Gianluca Bontempi Mauro Birattari Hugues Bersini

We introduce and discuss a local method to learn one-step-ahead predictors for iterated time series forecasting. For each single one-step-ahead prediction, our method selects among diierent alternatives a local model representation on the basis of a local cross-validation procedure. In the literature , local learning is generally used for function estimation tasks which do not take temporal beh...

2015
Kheir Eddine Farfar Mohamed Tarek Khadir Oussama Laib

Knowing that electrical load is a non storable resource; short term electric load forecasting becomes an important tool to optimise dispatching of electrical load in regular system planning. Several techniques have been used to accomplish this task, from traditional linear regression and BoxJenkins to artificial intelligence approaches such as Artificial Neural Networks (ANN). This work present...

2009
M. K. Akhtar G. A. Corzo S. J. van Andel

This paper explores the use of flow length and travel time as a pre-processing step for incorporating spatial precipitation information into Artificial Neural Network (ANN) models used for river flow forecasting. Spatially distributed precipitation is commonly required when modelling large basins, and it is usually incorporated in distributed physically-based hydrological modelling approaches. ...

نمودار تعداد نتایج جستجو در هر سال

با کلیک روی نمودار نتایج را به سال انتشار فیلتر کنید