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

Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers. This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy

Artificial Neural Networks in Agriculture

Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible

Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others

A Comparative Study of Vehicle Platoon with Limited Output Information in Directed Topologies

This paper aims to study and compare the effect of limited-output information in various directed topology to the performance of vehicle platoon. Two distributed controllers based on limited-output information will be compared to cooperative state variable feedback control which designed based on full-state information. The comparison will be conducted for four common directed topologies in the vehicle platoon application. Simulation analysis is performed in three scenarios, namely under normal operations, when the leader moves with constant acceleration and when the platoon is subjected to constant communication delay. Performances comparison will be observed from inter-vehicular distance response in each follower and the results will be displayed with respect to the follower vehicle index in the platoon configuration. Finally, the behavior of each control scheme in various topologies will be summarized

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped upon decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons

With the globally increasing electricity demand, its related uncertainties are on the rise as well. Therefore, a deeper insight of load forecasting techniques for projecting future electricity demands becomes imperative for business entities and policy makers. The electricity demand is governed by a set of different variables or “electricity demand determinants”. These demand determinants depend on forecasting horizons (long term, medium term, and short term), the load aggregation level, climate, and socio-economic activities. In this paper, a review of different electricity demand forecasting methodologies is provided in the context of a group of low and middle income countries. The article presents a comprehensive literature review by tabulating the different demand determinants used in different countries and forecasting the trends and techniques used in these countries. A comparative review of these forecasting methodologies over different time horizons reveals that the time series modeling approach has been extensively used while forecasting for long and medium terms. For short term forecasts, artificial intelligence-based techniques remain prevalent in the literature. Furthermore, a comparative analysis of the demand determinants in these countries indicates a frequent use of determinants like the population, GDP, weather, and load data over different time horizons. Following the analysis, potential research gaps are identified, and recommendations are provided, accordingly

Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management

The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management

Modeling and Control Techniques in Smart Systems

Energy and food crisis are two major problems that our human society has to face in the 21st century. With the world’s population reaching 7.62 billion as of May 2018, both electric power and agricultural industries turn to technological innovations for solutions to keep up the increasing demand. In the past and currently, utility companies rely on rule of thumb to estimate power consumption. However, inaccurate predictions often result in over production, and much energy is wasted. On the other hand, traditional periodic and threshold based irrigation practices have also been proven outdated. This problem is further compounded by recent years’ frequent droughts across the globe. New technologies are needed to manage irrigations more efficiently. Fortunately, with the unprecedented development of Artificial Intelligence (AI), wireless communication, and ubiquitous computing technologies, high degree of information integration and automation are steadily becoming reality. More smart metering devices are installed today than ever before, enabling fast and massive data collection. Patterns and trends can be more accurately predicted using machine learning techniques. Based on the results, utility companies can schedule production more efficiently, not only enhancing their profitabilities, but also making our world’s energy supply more sustainable. In addition, predictions can serve as references to detect anomalous activities like power theft and cyber attacks. On the other hand, with wireless communication, real-time soil moisture sensor readings and weather forecasts can be collected for precision irrigation. Smaller but more powerful controllers provide perfect platforms for complicated control algorithms. We designed and built a fully automated irrigation system at Bushland, Texas. It is designed to operate without any human intervention. Workers can program, move, and monitor multiple irrigation systems remotely. The algorithm that runs on the controls deserves more attention. AI and other state of art controlling techniques are implemented, making it much more powerful than any existing systems. By integrating professional crop yield simulation models like DSSAT, computers can run tens of thousand simulations on all kinds of weather and soil conditions, and more importantly, learn from the experience. In reality, such process would take thousands of years to obtain. Yet, the computers can find an optimum solution in minutes. The experience is then summarized as a policy and stored inside the controller as a lookup table. Furthermore, after each crop season, users can calibrate and update current policy with real harvest data. Crop yield models like DSSAT and AquaCrop play very important roles in agricultural research. They represent our best knowledge in plant biology and can be very accurate when well calibrated. However, the calibration process itself is often time consuming, thus limiting the scale and speed of using these models. We made efforts to combine different models to produce a single accurate prediction using machine learning techniques. The process does not require manual calibration, but only soil, historical weather, and harvest data. 20 models were built, and their results were evaluated and compared. With high accuracy, machine learning techniques have shown a promising direction to best utilize professional models, and demonstrated great potential for use in future agricultural research