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Challenges and Research Trends of Computational Intelligence

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 10726

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Special Issue Editors

Prof. Dr. Anastasios Dounis

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Department of Biomedical Engineering, University of West Attica, Agiou Spyridonos 17, 12243 Egaleo Park Campus, Greece
Interests: computational intelligence; evolutionary computing; fuzzy systems; fuzzy modelling; neural computing; intelligent control; intelligent energy management systems in buildings/hospitals; intelligence technologies in biomedical engineering; machine learning
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Prof. Dr. Ioannis Kalatzis

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Department of Biomedical Engineering, University of West Attica, Agiou Spyridonos 17, 12243 Egaleo Park Campus, Greece
Interests: biomedical image; signal processing and analysis with focus in pattern recognition; artificial intelligence applications in medicine and biology

Prof. Dr. Pantelis Asvestas

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Guest Editor

Department of Biomedical Engineering, University of West Attica, Agiou Spyridonos 17, 12243 Egaleo Park Campus, Greece
Interests: classification of biosignals and medical images using artificial intelligence; image processing; registration and fusion of medical data; 3D visualization of medical data; biometrics; real time video processing; fractal analysis of images

Special Issue Information

Dear Colleagues,

Computational Intelligence (CI) is a rapidly growing multidisciplinary field. Traditionally the main synergetic pillars of Computational Intelligence have been Neural Networks, Fuzzy Systems, Evolutionary Computation and Probabilistic Reasoning. However, in time many nature inspired computing paradigms have evolved. Thus, CI is an evolving field with challenges and research trends that encompasses computing paradigms like ambient intelligence, artificial life, cultural learning, artificial endocrine networks, social reasoning and artificial hormone networks. CI has significantly evolved and reached high levels of hybridization and complexity and plays a major role in developing successful intelligent systems, including control systems and smart energy management systems. The latest evolution of CI technologies has been driving the industrial and societal transformation, where are widely developed of plethora applications. Energy industry is at the edge of an intelligent transformation with the computational intelligence techniques in cutting edge.

Topics of interest include, but are not limited to:
CI techniques:

Chaos theory;
Evolutionary computing;
Fuzzy computing;
Hybrid intelligence computing;
Morphic computing;
Nature-inspired computing;
Neural computing;
Probabilistic computing;

CI applications:

Knowledge and data mining;
Agents and multiagent systems;
Machine learning;
Ambient intelligence;
Decision-making and support systems;
Image processing;
Robotics;
Renewable energy sources；
Optimization in energy systems;
Energy in Healthcare；
Bio-inspired systems;
Biomedical engineering;
Fault diagnosis and prediction in energy systems;
Smart energy microgrids;
Intelligent agents;
Smart buildings;
Power and energy;
Intelligent energy consumption in smart hospitals;
Intelligent forecasting models;
Pattern recognition;
Internet of Energy；
Reinforcement learning.

Prof. Dr. Anastasios Dounis
Prof. Dr. Ioannis Kalatzis
Prof. Dr. Pantelis Asvestas
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (6 papers)

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Research

31 pages, 800 KiB

Open AccessArticle

Customised Multi-Energy Pricing: Model and Solutions

by Qiuyi Hong, Fanlin Meng and Jian Liu

Energies 2023, 16(4), 2080; https://0-doi-org.brum.beds.ac.uk/10.3390/en16042080 - 20 Feb 2023

Viewed by 1888

Abstract

With the increasing interdependence among energies (e.g., electricity, natural gas and heat) and the development of a decentralised energy system, a novel retail pricing scheme in the multi-energy market is demanded. Therefore, the problem of designing a customised multi-energy pricing scheme for energy retailers is investigated in this paper. In particular, the proposed pricing scheme is formulated as a bilevel optimisation problem. At the upper level, the energy retailer (leader) aims to maximise its profit. Microgrids (followers) equipped with energy converters, storage, renewable energy sources (RES) and demand response (DR) programs are located at the lower level and minimise their operational costs. Three hybrid algorithms combining metaheuristic algorithms (i.e., particle swarm optimisation (PSO), genetic algorithm (GA) and simulated annealing (SA)) with the mixed-integer linear program (MILP) are developed to solve the proposed bilevel problem. Numerical results verify the feasibility and effectiveness of the proposed model and solution algorithms. We find that GA outperforms other solution algorithms to obtain a higher retailer’s profit through comparison. In addition, the proposed customised pricing scheme could benefit the retailer’s profitability and net profit margin compared to the widely adopted uniform pricing scheme due to the reduction in the overall energy purchasing costs in the wholesale markets. Lastly, the negative correlations between the rated capacity and power of the energy storage and both retailer’s profit and the microgrid’s operational cost are illustrated. Full article

(This article belongs to the Special Issue Challenges and Research Trends of Computational Intelligence)

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16 pages, 4424 KiB

Open AccessArticle

Solar and Wind Quantity 24 h—Series Prediction Using PDE-Modular Models Gradually Developed according to Spatial Pattern Similarity

by Ladislav Zjavka

Energies 2023, 16(3), 1085; https://0-doi-org.brum.beds.ac.uk/10.3390/en16031085 - 18 Jan 2023

Cited by 4 | Viewed by 1411

Abstract

The design and implementation of efficient photovoltaic (PV) plants and wind farms require a precise analysis and definition of specifics in the region of interest. Reliable Artificial Intelligence (AI) models can recognize long-term spatial and temporal variability, including anomalies in solar and wind patterns, which are necessary to estimate the generation capacity and configuration parameters of PV panels and wind turbines. The proposed 24 h planning of renewable energy (RE) production involves an initial reassessment of the optimal day data records based on the spatial pattern similarity in the latest hours and their follow-up statistical AI learning. Conventional measurements comprise a larger territory to allow the development of robust models representing unsettled meteorological situations and their significant changes from a comprehensive aspect, which becomes essential in middle-term time horizons. Differential learning is a new unconventionally designed neurocomputing strategy that combines differentiated modules composed of selected binomial network nodes as the output sum. This approach, based on solutions of partial differential equations (PDEs) defined in selected nodes, enables us to comprise high uncertainty in nonlinear chaotic patterns, contingent upon RE local potential, without an undesirable reduction in data dimensionality. The form of back-produced modular compounds in PDE models is directly related to the complexity of large-scale data patterns used in training to avoid problem simplification. The preidentified day-sample series are reassessed secondary to the training applicability, one by one, to better characterize pattern progress. Applicable phase or frequency parameters (e.g., azimuth, temperature, radiation, etc.) are related to the amplitudes at each time to determine and solve particular node PDEs in a complex form of the periodic sine/cosine components. The proposed improvements contribute to better performance of the AI modular concept of PDE models, a cable to represent the dynamics of complex systems. The results are compared with the recent deep learning strategy. Both methods show a high approximation ability in radiation ramping events, often in PV power supply; moreover, differential learning provides more stable wind gust predictions without undesirable alterations in day errors, namely in over-break frontal fluctuations. Their day average percentage approximation of similarity correlation on real data is 87.8 and 88.1% in global radiation day-cycles and 46.7 and 36.3% in wind speed 24 h. series. A parametric C++ executable program with complete spatial metadata records for one month is available for free to enable another comparative evaluation of the conducted experiments. Full article

(This article belongs to the Special Issue Challenges and Research Trends of Computational Intelligence)

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16 pages, 4003 KiB

Open AccessArticle

Electric Vehicle Charging Schedules in Workplace Parking Lots Based on Evolutionary Optimization Algorithm

by Stavros Poniris and Anastasios I. Dounis

Energies 2023, 16(1), 221; https://0-doi-org.brum.beds.ac.uk/10.3390/en16010221 - 25 Dec 2022

Cited by 2 | Viewed by 1226

Abstract

The electrification of vehicles is considered to be the means of reducing the greenhouse gas (GHG) emissions of the transport sector, but “range anxiety” makes most people reluctant to adopt electric vehicles (EVs) as their main method of transportation. Workplace charging has been proven to counter range anxiety and workplace charging is becoming quite common. A workplace parking lot can house hundreds of EVs. In this paper, a program has been developed in MATLAB that uses the well-known evolutionary optimization algorithm, the genetic algorithm (GA), to optimize the charging schedule of fifty EVs that aims at achieving three goals: (a) keeping the electricity demand low, (b) reducing the cost of charging and (c) applying load shifting. Three schedules were developed for three scenarios. The results demonstrate that each schedule was successful in achieving its goal, which means that scheduling the charging of a fleet of EVs can be used as a method of demand-side management (DSM) in workplace parking lots and at the same time reduce the energy cost of charging. In the scenarios examined in this paper, cost was reduced by approximately 2%. Full article

(This article belongs to the Special Issue Challenges and Research Trends of Computational Intelligence)

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20 pages, 832 KiB

Open AccessArticle

Charging Stations and Electromobility Development: A Cross-Country Comparative Analysis

by Tomasz Zema, Adam Sulich and Sebastian Grzesiak

Energies 2023, 16(1), 32; https://0-doi-org.brum.beds.ac.uk/10.3390/en16010032 - 21 Dec 2022

Cited by 7 | Viewed by 2170

Abstract

The Industry 4.0 idea influences the development of both charging stations and electromobility development, due to its emphasis on device communication, cooperation, and proximity. Therefore, in electromobility development, growing attention is paid to chargers’ infrastructure density and automotive electric vehicles’ accessibility. The main goal of this scientific paper was to present the electromobility development represented in the number of charging stations and its infrastructure development calculations. In this study, the sequence of methods was used to indicate and explore the research gap. The first was the Structured Literature Review (SLR) variation method. The second method was the classical tabular comparison of gathered results. The third research method was a cluster analysis based on secondary data with cross-country comparisons of the number of charging stations and electric cars. Therefore, this paper presents a theoretical discussion and practical business implications based on the achieved results of clusters and rankings. The main finding of this paper is that charging stations play a pivotal role in electromobility development in countries with already developed road infrastructure and maritime transportation. The charging stations can support energetic infrastructure, especially in countries with vast geographical distances. The charging stations and electric vehicles statistics presented in ratios and ranks proved similarities in the electromobility development patterns in the analyzed countries. This paper also presents the limitations of the performed study and identifies possible future research avenues. Full article

(This article belongs to the Special Issue Challenges and Research Trends of Computational Intelligence)

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25 pages, 30402 KiB

Open AccessArticle

Comparison of Hospital Building’s Energy Consumption Prediction Using Artificial Neural Networks, ANFIS, and LSTM Network

by Dimitrios K. Panagiotou and Anastasios I. Dounis

Energies 2022, 15(17), 6453; https://0-doi-org.brum.beds.ac.uk/10.3390/en15176453 - 03 Sep 2022

Cited by 11 | Viewed by 1710

Abstract

Since accurate load forecasting plays an important role in the improvisation of buildings and as described in EU’s “Green Deal”, financial resources saved through improvisation of the efficiency of buildings with social importance such as hospitals, will be the funds to support their mission, the social impact of load forecasting is significant. In the present paper, eight different machine learning predictors will be examined for the short-term load forecasting of a hospital’s facility building. The challenge is to qualify the most suitable predictors for the abovementioned task, which is beneficial for an in-depth study on accurate predictors’ applications in Intelligent Energy Management Systems (IEMS). Three Artificial Neural Networks using a backpropagation algorithm, three Artificial Neural Networks using metaheuristic optimization algorithms for training, an Adaptive Neuro-Fuzzy Inference System (ANFIS), and a Long-Short Term Memory (LSTM) network were tested using timeseries generated from a simulated healthcare facility. ANFIS and backpropagation-based trained models outperformed all other models since they both deal well with complex nonlinear problems. LSTM also performed adequately. The models trained with metaheuristic algorithms demonstrated poor performance. Full article

(This article belongs to the Special Issue Challenges and Research Trends of Computational Intelligence)

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18 pages, 4679 KiB

Open AccessArticle

ReNFuzz-LF: A Recurrent Neurofuzzy System for Short-Term Load Forecasting

by George Kandilogiannakis, Paris Mastorocostas and Athanasios Voulodimos

Energies 2022, 15(10), 3637; https://0-doi-org.brum.beds.ac.uk/10.3390/en15103637 - 16 May 2022

Cited by 4 | Viewed by 1381

Abstract

A neurofuzzy system is proposed for short-term electric load forecasting. The fuzzy rule base of ReNFuzz-LF consists of rules with dynamic consequent parts that are small-scale recurrent neural networks with one hidden layer, whose neurons have local output feedback. The particular representation maintains the local learning nature of the typical static fuzzy model, since the dynamic consequent parts of the fuzzy rules can be considered as subsystems operating at the subspaces defined by the fuzzy premise parts, and they are interconnected through the defuzzification part. The Greek power system is examined, and hourly based predictions are extracted for the whole year. The recurrent nature of the forecaster leads to the use of a minimal set of inputs, since the temporal relations of the electric load time-series are identified without any prior knowledge of the appropriate past load values being necessary. An extensive simulation analysis is conducted, and the forecaster’s performance is evaluated using appropriate metrics (APE, RMSE, forecast error duration curve). ReNFuzz-LF performs efficiently, attaining an average percentage error of 1.35% and an average yearly absolute error of 86.3 MW. Finally, the performance of the proposed forecaster is compared to a series of Computational Intelligence based models, such that the learning characteristics of ReNFuzz-LF are highlighted. Full article

(This article belongs to the Special Issue Challenges and Research Trends of Computational Intelligence)

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