Artificial intelligence models have been widely applied for natural gas consumption forecasting over the past decades, especially short-term forecasting. This paper proposes a three-layer neural network model that can extract key information from input factors and improve weight optimization mechanism of long memory (LSTM) to effectively forecast consumption. In proposed model, convolutional (C...

The electricity consumption of a terminal is mainly related to the number of container movements and the weather of each day. With the introduction of electric mobility for heavy duty container carriers at a seaport container terminal short-term load forecasting gains an important part in the procurement process. This paper describes a case-based approach to the forecasting of the electricity c...

In this paper, a forecasting-based dynamic virtual channel allocation technique for reducing the power consumption of network on chips is introduced. Based on the network traffic as well as past link and total virtual channel utilizations, the technique dynamically forecasts the number of virtual channels that should be active. It is based on an exponential smoothing forecasting method that fil...

We consider the problem of short-term peak demand forecasting in a district heating system. Our dataset consists of four separated periods, with 198 days each period and 24 hourly observations within each day relative to heat consumption and climate. We take advantage of the functional nature of the data and we propose a forecasting methodology based on functional regression. The influence of e...

The research is motivated by the need for economic efficiency and risk management in the national electric system. Stochastic costs of natural gas are introduced in a generalized network flow model of the integrated power energy system to explore the effects of uncertain fuel costs on the optimal energy flows in U.S. The fuel costs are modeled as discretely distributed random variables and a ro...

In this study, we estimate variable fuel costs and total energy consumption for grid-powered electric cars using eight-year forecasts of cost per kilowatt hour for US national data coupled with real data distributions derived by daily electronic tracking the performance of a single Nissan Leaf located in Texas. These fuel costs are estimated based on a stochastic driving distance, then costs ar...