Load Forecasting plays a key role in making today's and future's Smart Energy Grids sustainable and reliable. Accurate power consumption prediction allows utilities to organize in advance their resources or to execute Demand Response strategies more effectively, which enables several features such as higher sustainability, better quality of service, and affordable electricity tariffs. It is eas...

We propose the use of Google online search data for nowcasting and forecasting the number of food stamps recipients. We perform a large out-of-sample forecasting exercise with almost 3000 competing models with forecast horizons up to 2 years ahead, and we show that models including Google search data statistically outperform the competing models at all considered horizons. These results hold al...

For modeling of change of terminal server load, the approach including representation of time series of server parameters in the form of fuzzy time series is used. Further in the article the analysis of fuzzy time series is considered. The model of fuzzy tendencies is offered for terminal server traffic modeling. This model reflects change of the volume of terminal server traffic expressed ling...

Social media-based forecasting has received significant attention from academia and industries in recent years. With a focus on Twitter, this paper investigates whether sentiments of the tweets regarding the 7 largest US financial service companies (in U.S. dollars) are related to the stock price changes of these companies. The authors’ findings indicate, in the financial services context, nega...

Evolutionary training methods for Artificial Neural Networks can escape local minima. Thus, they are useful to train recurrent neural networks for short-term weather forecasting. However, these algorithms are not guaranteed to converge fast or even converge at all due to their stochastic nature. In this paper, we present an algorithm that uses implicit gradient information and is able to train ...

Accurately predicting non-peak traffic is crucial to daily traffic for all forecasting models. In the paper, least squares support vector machines (LS-SVMs) are investigated to solve such a practical problem. It is the first time to apply the approach and analyze the forecast performance in the domain. For comparison purpose, two parametric and two non-parametric techniques are selected because...

The electric power industry is currently undergoing an unprecedented reform. One of the most exciting and potentially profitable recent developments is increasing usage of artificial intelligence techniques. The intention of this paper is to give an overview of using neural network (NN) techniques in power systems. According to the growth rate of NNs application in some power system subjects, t...

This paper presents a feedforward neural network approach to sunspot forecasting. The sunspot series were analyzed with feedforward neural networks, formalized based on statistical models. The statistical models were used as comparison models along with recurrent neural networks. The feedforward networks had 24 inputs (depending on the number of predictor variables), one hidden layer with 20 ...

This paper proposes a novel neural model to the problem of short-term load forecasting. The neural model is made up of two self-organizing map nets — one on top of the other. It has been successfully applied to domains in which the context information given by former events plays a primary role. The model was trained and assessed on load data extracted from a Brazilian electric utility. It was ...

Data mining applications in agriculture is a relatively new approach for forecasting / predicting of agricultural crop/animal management. In the present study an attempt has been made to study the influence of climatic parameters on soybean productivity using decision tree induction technique. The findings of Decision tree were framed into different rules for better understanding by the end use...