Weather information is an important factor in short-term load forecasting (STLF). However, for a long time, more importance has always been attached to forecasting models instead of other processes such as the introduction of weather factors or feature selection for STLF. The main aim of this paper is to develop a novel methodology based on Fisher information for meteorological variables introd...

Catastrophic disasters afflicting human society are often triggered by tsunamis, earthquakes, widespread flooding, and weather and climate events. As human populations increasingly move into geographic areas affected by these earth system hazards, forecasting the onset of these large and damaging events has become increasingly urgent. In this paper we consider the fundamental problem of forecas...

A novel method is proposed to retrieve image sequences with the goal of forecasting complex and timevarying natural patterns. To that end, we introduce a framework called Memory-Based Forecasting; it provides forecast information based on the temporal development of past retrieved sequences. This paper targets the radar echo patterns in weather radar images, and aims to realize an image retriev...

An attempt is made in this paper to develop an Enhanced Support Vector Regression (ESVR ) model with more un-interpretable kernel functions in the domain of forecasting the weather conditions. Every predicate model takes input data set parameters, processing with in specified levels of classification into variable sets and countered with a variable set reduction to reach the decision of predict...

Weather forecasting has become an important field of research in the last few decades. In most of the cases the researcher had attempted to establish a linear relationship between the input weather data and the corresponding target data. But with the discovery of nonlinearity in the nature of weather data, the focus has shifted towards the nonlinear prediction of the weather data. Although, the...

Chaos is a fundamental property that possesses nonlinearity and a sensitive dependence on initial conditions. Because of the nonlinearity in a chaotic system it becomes very difficult to make accurate predictions about the system over a given time interval. Weather forecasting is an example of how chaos theory effects the accuracy of predictions over a given time interval. Through analyzing a w...

In this work, we will identify the existence of “rough dependence on initial conditions” in atmospheric phenomena, a concept which is a problem for weather analysis and forecasting. Typically, two initially similar atmospheric states will diverge slowly over time such that forecasting the weather using the Navier-Stokes equations is useless after some characteristic time scale. With rough depen...

Weather forecasting is crucial to both the demand and supply sides of electricity markets. Temperature has a great effect on energy demand. Moreover, solar and wind are very promising renewable energy sources. In this paper, a large vector autoregression (VAR) model is built to forecast three important weather variables for 61 cities around the United States. We estimate the VAR model with 16 y...

The decision-making literature contains considerable information about how humans approach tasks involving uncertainty using heuristics. Although there is some reason to believe that weather forecasters are not identical in all respects to the typical subjects used in judgment and decision-making studies, there also is evidence that weather forecasters are not so different that the existing und...