Deep learning approaches have reached a celebrity status in artificial intelligence field, its success have mostly relied on Convolutional Networks (CNN) and Recurrent Networks. By exploiting fundamental spatial properties of images and videos, the CNN always achieves dominant performance on visual tasks. And the Recurrent Networks (RNN) especially long short-term memory methods (LSTM) can succ...

The realization of load forecasting studies within the scope periods varies depending on application areas and estimation purposes. It is mainly carried out at three intervals: short-term, medium-term, long-term. Short-term (STLF) incorporates hour-ahead forecasting, which critical for dynamic data-driven smart power system applications. Nevertheless, based our knowledge, there are not enough a...

Recently, there has been a significant amount of interest in satellite telemetry anomaly detection (AD) using neural networks (NN). For AD purposes, the current approaches focus on either forecasting or reconstruction time series, and they cannot measure level reliability probability correct detection. Although Bayesian network (BNN)-based are well known for series uncertainty estimation, compu...

The modeling of the relationships between the power loads and the variables that influence the power loads especially in the abnormal days is the key point to improve the performance of short-term load forecasting systems. To integrate the advantages of several forecasting models for improving the forecasting accuracy, based on data mining and artificial neural network techniques, an ensemble d...

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...

Mediumand long-term runoff forecasting is essential for hydropower generation and water resources coordinated regulation in the Yellow River headwaters region. Climate change has a great impact on runoff within basins, and incorporating different climate information into runoff forecasting can assist in creating longer lead-times in planning periods. In this paper, a multimodel approach was dev...

Accurate demand forecasts are important for managing energy efficiently in electric grids. However, building models for demand forecasting is a challenging task as it depends on numerous factors that are both intrinsic and external to the grid. Furthermore, these factors are time-varying and non-linear as well. This makes demand forecasting a cumbersome task. This investigation proposes a simpl...

Background and Objectives: Halal branding covers a wide range of economic activities including pharmaceuticals, cosmetics, health, textiles, clothing, leather, financial services, banking and tourism. Among the various forms of tourism, medical tourism has grown rapidly due to its competitive advantages. Therefore, the purpose of this study is to investigate halal branding as a strategy to deve...

The emergence of online product review forums has enabled firms to monitor consumer opinions about their products in real-time by mining publicly available information from the Internet. This paper studies the value of online product ratings in revenue forecasting of new experience goods. Our objective is to understand what metrics of online ratings are the most informative indicators of a prod...

Today’s economy is characterized by increased competition, faster product development and increased product differentiation. As a consequence product lifecycles become shorter and demand patterns become more volatile which especially affects the retail industry. This new situation imposes stronger requirements on demand forecasting methods. Due to shorter product lifecycles historical sales inf...