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

Artificial Neural Networks for Short-Term Load Forecasting in Microgrids Environment Energy

The adaptation of energy production to demand has been traditionally very important for utilities in order to optimize resource consumption. This is especially true also in microgrids where many intelligent elements have to adapt their behaviour depending on the future generation and consumption conditions. However, traditional forecasting has been performed only for extremely large areas, such as nations and regions. This work aims at presenting a solution for short-term load forecasting (STLF) in microgrids, based on a three-stage architecture which starts with pattern recognition by a self-organizing map (SOM), a clustering of the previous partition via k-means algorithm, and finally demand forecasting for each cluster with a multilayer perceptron. Model validation was performed with data from a microgrid-sized environment provided by the Spanish company Iberdrola. (C) 2014 Elsevier Ltd. All rights reserved.Hernandez, L.; Baladron, C.; Aguiar, JM.; Carro, B.; Sanchez-Esguevillas, A.; Lloret, J. (2014). Artificial Neural Networks for Short-Term Load Forecasting in Microgrids Environment Energy. Energy. 75:252-264. doi:10.1016/j.energy.2014.07.065S2522647

Smart Water: Short-Term Forecasting Application in Water Utilities

The unyielding interconnection between water and energy has made demand forecasting a necessity for water utilities. Electricity prices driven by the time of use has impelled water utilities towards short-term water demand forecasting. The progressive new Smart Water Grid platform has helped water utilities in utilizing their Water Distribution Networks. This two-way platform has provided developers and decision makers with robust models that rely on consumer feedback. Among these models is the water demand forecasting models. Multitudinous demand forecasting methods have been developed but none have utilized model implementation practicality. Utilities differ in size, capacity, and interest. While small size utilities focus on model simplicity, larger utilities prioritize model accuracy. This work focuses on a water utility located in Essex County, Ontario, Canada. This study presents three papers that focus on investigation and evaluation of short-term water demand forecasting techniques. The first paper compares water usage between two crops (tomatoes and bell peppers) in an effort to evaluate a crop to crop forecast technique that relies on one crops watering data in order to produce forecasts for another crop, The second paper examines the effect of model type, input type, and data size on model performance and computational load. The third paper proposes a new methodology where model performance is not sacrificed for model simplification

Online Bearing Remaining Useful Life Prediction Based on a Novel Degradation Indicator and Convolutional Neural Networks

In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs are of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are often limited by the complexity of mathematical modeling. Conventional data-driven approaches, on the other hand, require massive efforts to extract the degradation features and construct health index. In this paper, a novel online data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNN) in predicting the RUL of bearings. More concretely, the raw vibrations of training bearings are first processed using the Hilbert-Huang transform (HHT) and a novel nonlinear degradation indicator is constructed as the label for learning. The CNN is then employed to identify the hidden pattern between the extracted degradation indicator and the vibration of training bearings, which makes it possible to estimate the degradation of the test bearings automatically. Finally, testing bearings' RULs are predicted by using a $\epsilon$-support vector regression model. The superior performance of the proposed RUL estimation framework, compared with the state-of-the-art approaches, is demonstrated through the experimental results. The generality of the proposed CNN model is also validated by transferring to bearings undergoing different operating conditions

A hybrid model of self-organizing map and deep learning with genetic algorithm for managing energy consumption in public buildings

Mohamed, A. A., Santos, V., Dias, M. S., & Mahmoud, A. N. (2024). A hybrid model of self-organizing map and deep learning with genetic algorithm for managing energy consumption in public buildings. Journal of Cleaner Production, 1-28. https://doi.org/10.1016/j.jclepro.2023.140040 --- This work has been supported by Portuguese funds through FCT - Fundação para a Ciência e Tecnologia, I.P., under the project FCT UIDB/04466/2020, and this work has been supported by Information Management Research Center (MagIC) - NOVA Information Management SchoolIntelligent applications have become essential for effectively managing energy consumption in public buildings. Energy management, especially for critical infrastructure buildings and public buildings, often serves as an example of sustainable practices since it tries to increase energy resilience through backup power systems. These structures were dealing with more difficulties, such as tight budgets, complicated regulations, behavioral issues, and others. Managing energy consumption in buildings is a multifaceted undertaking that entails grappling with non-uniform energy usage and a lack of established design guidelines for implementing energy-efficient and sustainable solutions. Consequently, analyzing energy utilization trends in public buildings and projecting future energy requirements is of paramount importance. This comprehension is indispensable to the identification and acknowledgment of energy consumption patterns in commercial and institutional buildings. Our research is highly significant for optimizing energy usage, reducing environmental impact, and making informed decisions in building management. We emphasize the importance of our predictive capabilities, sustainability, and life cycle assessment (LCA) techniques in achieving these objectives. Our hybrid model improves energy efficiency while also making a significant contribution to the larger objective of meeting sustainability standards in the built environment through thorough analyses of real-world data. The goal of this study is to determine the most sensible classification and forecasting scheme for the energy usage rates of public structures. The goal of this study is to determine the most sensible classification and forecasting scheme for the energy usage rates of public structures. In order to determine the number of energy consumption pattern clusters, a Self-Organizing Map (SOM) model was utilized in conjunction with Principal Component Analysis (PCA). The determination of clustering levels for each structure was executed by utilizing K-means in conjunction with a Genetic algorithm (GA). This approach enabled the identification of optimal clustering levels for the given structures, thereby facilitating effective analysis and interpretation of the data. The GA was specifically employed to determine the optimal centroid points for each cluster, thus optimizing the performance of the fitting model. Cluster analysis pattern extraction has made determining which buildings consume the most energy easier. As intelligent models for predicting energy usage, Convolutional Neural Networks (CNNs) and CNNs paired with a GA have also been used. The application of a GA to modify multiple settings of a CNN was conducted at this stage. The CNN model using a GA shows the method that was used provides higher accuracy and standard error compared to the conventional approach. It achieves a 94.01% accuracy on the training dataset and a 93.74% accuracy on the validation dataset with an error rate of 0.24 and 0.26 respectively. On the training dataset, the accuracy is 89.03% with a standard error of 0.3, whereas on the validation dataset, the accuracy is 88.91% with a 0.33 standard error. This research can help policymakers in the energy sector make better decisions regarding the timing of energy supply and demand for public buildings.publishersversionpublishe

CPS Data Streams Analytics based on Machine Learning for Cloud and Fog Computing: A Survey

Cloud and Fog computing has emerged as a promising paradigm for the Internet of things (IoT) and cyber-physical systems (CPS). One characteristic of CPS is the reciprocal feedback loops between physical processes and cyber elements (computation, software and networking), which implies that data stream analytics is one of the core components of CPS. The reasons for this are: (i) it extracts the insights and the knowledge from the data streams generated by various sensors and other monitoring components embedded in the physical systems; (ii) it supports informed decision making; (iii) it enables feedback from the physical processes to the cyber counterparts; (iv) it eventually facilitates the integration of cyber and physical systems. There have been many successful applications of data streams analytics, powered by machine learning techniques, to CPS systems. Thus, it is necessary to have a survey on the particularities of the application of machine learning techniques to the CPS domain. In particular, we explore how machine learning methods should be deployed and integrated in cloud and fog architectures for better fulfilment of the requirements, e.g. mission criticality and time criticality, arising in CPS domains. To the best of our knowledge, this paper is the first to systematically study machine learning techniques for CPS data stream analytics from various perspectives, especially from a perspective that leads to the discussion and guidance of how the CPS machine learning methods should be deployed in a cloud and fog architecture

A systematic literature review using text mining and bibliometric analysis

109 “Consumo SMART” https://www.simplex.gov.pt/medidas. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.The high level of energy consumption of buildings is significantly influencing occupant behavior changes towards improved energy efficiency. This paper introduces a systematic literature review with two objectives: to understand the more relevant factors affecting energy consumption of buildings and to find the best intelligent computing (IC) methods capable of classifying and predicting energy consumption of different types of buildings. Adopting the PRISMA method, the paper analyzed 822 manuscripts from 2013 to 2020 and focused on 106, based on title and abstract screening and on manuscripts with experiments. A text mining process and a bibliometric map tool (VOS viewer) were adopted to find the most used terms and their relationships, in the energy and IC domains. Our approach shows that the terms “consumption,” “residential,” and “electricity” are the more relevant terms in the energy domain, in terms of the ratio of important terms (TITs), whereas “cluster” is the more commonly used term in the IC domain. The paper also shows that there are strong relations between “Residential Energy Consumption” and “Electricity Consumption,” “Heating” and “Climate. Finally, we checked and analyzed 41 manuscripts in detail, summarized their major contributions, and identified several research gaps that provide hints for further research.publishersversionpublishe