Computational intelligence approaches for energy load forecasting in smart energy management grids: state of the art, future challenges, and research directions and Research Directions

Energy management systems are designed to monitor, optimize, and control the smart grid energy market. Demand-side management, considered as an essential part of the energy management system, can enable utility market operators to make better management decisions for energy trading between consumers and the operator. In this system, a priori knowledge about the energy load pattern can help reshape the load and cut the energy demand curve, thus allowing a better management and distribution of the energy in smart grid energy systems. Designing a computationally intelligent load forecasting (ILF) system is often a primary goal of energy demand management. This study explores the state of the art of computationally intelligent (i.e., machine learning) methods that are applied in load forecasting in terms of their classification and evaluation for sustainable operation of the overall energy management system. More than 50 research papers related to the subject identified in existing literature are classified into two categories: namely the single and the hybrid computational intelligence (CI)-based load forecasting technique. The advantages and disadvantages of each individual techniques also discussed to encapsulate them into the perspective into the energy management research. The identified methods have been further investigated by a qualitative analysis based on the accuracy of the prediction, which confirms the dominance of hybrid forecasting methods, which are often applied as metaheurstic algorithms considering the different optimization techniques over single model approaches. Based on extensive surveys, the review paper predicts a continuous future expansion of such literature on different CI approaches and their optimizations with both heuristic and metaheuristic methods used for energy load forecasting and their potential utilization in real-time smart energy management grids to address future challenges in energy demand managemen

Dynamics of precipitation climatology of the Southwest Pacific Region

An analysis of ten-year mean precipitation data from 1990 – 1999 is presented, that examines precipitation trends for the western Pacific region, bounded by 155˚ E – 135˚ W, 15˚ N – 30˚ S. Wind fields derived from the NCEP/NCAR reanalaysis data are presented for the same period of study to examine the nature of the airflow for study region. To investigate the possible impacts of convergence and divergence on precipitation distribution, surface and 200 hPa divergence fields are derived using the wind fields and correlated with the precipitation data. Correlation between mean monthly Southern Oscillation Index (SOI) and precipitation is also carried out. The precipitation patterns reveal several features that are prominent during the monthly and seasonal time scales. There is a rise in precipitation during highly negative surface divergences and a fall during positive surface divergences on the spatial map. It has been shown that summer shows the highest negative correlation between precipitation and surface divergence while autumn shows the least at the 5% level of significance. Correlation between SOI and precipitation indicates that highly positive correlation between the two variables exist along the zone of South Pacific Convergence Zone (SPCZ)

Artificial neural networks for prediction of Steadman Heat Index

This chapter aims to design and evaluate Artificial Neural Networks (ANN), an intelligent data analytic model to predict daily Steadman Heat Index (SHI) using temperature and humidity. Using 15 stations in Australia, trend analysis for the period 1950–2017 is performed using Mann–Kendal test statistics Sen’s slope methods. Twelve ANN models are developed with a three-layer network employing different combinations of the training algorithm, hidden transfer, and output function. The Levenberg–Marquardt and Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi-Newton backpropagation algorithms are utilized to determine the best combination of learning algorithms, hidden transfer, and output functions of the optimum ANN model. Assessment of model performance includes the spread and distribution of predicted SHI, Legates and McCabe Index, Mean Absolute Error, Root Mean Square Error, Coefficient of Determination, the Willmott’s Index of Agreement, and Nash–Sutcliffe Coefficient of Efficiency. The designed model appears to be a suitable intelligent data analytic tool for weather prediction, climate change studies, and probable evaluation of dry climatic conditions in the near future replying to historical datasets to model their future values. The findings have implications for disaster risk management particularly mitigating heatwave risk and consequences on human populations, ecosystems, and other areas including agricultural, health, and wellbeing

Computational intelligence approach for modeling hydrogen production: a review

Hydrogen is a clean energy source with a relatively low pollution footprint. However, hydrogen does not exist in nature as a separate element but only in compound forms. Hydrogen is produced through a process that dissociates it from its compounds. Several methods are used for hydrogen production, which first of all differ in the energy used in this process. Investigating the viability and exact applicability of a method in a specific context requires accurate knowledge of the parameters involved in the method and the interaction between these parameters. This can be done using top-down models relying on complex mathematically driven equations. However, with the raise of computational intelligence (CI) and machine learning techniques, researchers in hydrology have increasingly been using these methods for this complex task and report promising results. The contribution of this study is to investigate the state of the art CI methods employed in hydrogen production, and to identify the CI method(s) that perform better in the prediction, assessment and optimization tasks related to different types of Hydrogen production methods. The resulting analysis provides in-depth insight into the different hydrogen production methods, modeling technique and the obtained results from various scenarios, integrating them within the framework of a common discussion and evaluation paper. The identified methods were benchmarked by a qualitative analysis of the accuracy of CI in modeling hydrogen production, providing extensive overview of its usage to empower renewable energy utilization