Search

Summary: Computationally intelligent energy forecasting methods for appropriate energy management at the consumer/producer side have a positive impact on the preservation of energy and play a constructive role in tackling global climate change. The energy production and consumption are very high worldwide, demanding intelligent methods with real‐world implementation potentials for appropriate energy management. In this paper, we survey the existing intelligent load forecasting (ILF) systems, highlight their advantages and downsides, and briefly discuss the workflow of the employed literature. Furthermore, we debate on the existing load forecasting datasets and their features along with a brief overview of the challenges confronted by researchers using these datasets. Distinct from previous survey papers, we provide a detailed review of performance evaluation metrics and comparison of employed methods for energy load forecasting, thereby concluding the need of efficient, effective, and adoptable ILF methods functional in real‐world scenarios. Finally, we assess the employed techniques and deliver future research opportunities based on the derived conclusions from existing research works. This paper delivers the overall energy forecasting literature in a compact form with possible future insights for researchers working in ILF domain. [ABSTRACT FROM AUTHOR]

The iron and steel industry is a large energy user in the manufacturing sector. Carbon dioxide from the steel industry accounts for about 5–7% of the total anthropogenic CO2 emission. Concerns about energy consumption and climate change have been growing on the sustainability agenda of the steel industry. The CO2 emission will be heavily influenced with increasing steel production in the world. It is of great interest to evaluate and decrease the specific CO2 emission and to find out feasible solutions for its reduction. In this work, a process integration method focusing on the integrated steel plant system has been applied. In this paper, an optimization model, which can be used to evaluate CO2 emission for the integrated steel plant system, is presented. Two application cases of analysing CO2 emission reduction possibilities are included in the paper. Furthermore, the possibility to apply the model for a specific integrated steel plant has been discussed. The research work on the optimization of energy and CO2 emission has shown that it is possible to create a combined optimization tool that is powerful to assess the system performance from several aspects for the steel plant. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]