Special Issue "Bioinspired Computation for Sustainable Energy Systems"
Deadline for manuscript submissions: 31 May 2021.
Interests: bioinspired optimization; combinatorial optimization; artificial intelligence; metaheuristics; swarm intelligence
Special Issues and Collections in MDPI journals
Bioinspired computation has arisen as one of the most studied and quickly-growing topics in artificial intelligence. Some of the main influences behind the conception of this topic are the well-known Genetic Algorithm and the Ant Colony Optimization and Particle Swarm Optimization algorithms. These methods, and many similar ones, lit the fuse of the success of this area of knowledge, serving as the origin and principal inspiration of their subsequent research. This success has led to the proposal of additional novel algorithms, inspired by different sources such as the behavioral patterns of animals, social and political behaviors or physical processes. The proposal of new solvers evidences the capability of this type of algorithms to reach a near-optimal performance over a wide range of academic and real-world problems.
In the context of sustainable energy systems, the high increase of energy demand in the last century has led to a development of new renewable and sustainable energy sources, such as solar photovoltaic, wind, biomass, and geothermal energy, among others. In order to enhance the performance of these systems, modeling and optimization techniques have arisen to provide efficient solutions for optimal energy production, planning, storage, distribution, etc. In this regard, bioinspired computation tecniques are considered as a key enabling technology for coping with the above-mentioned challenges.
This Special Issue aims at disseminating the latest findings and research achievements in the areas of bioinspired optimization and sustainable energy systems, with the intention to balance between theoretical research ideas and their practicability as well as industrial applicability. To this end, scholars and practitioners from academia and industrial fields are invited to submit high-quality original contributions to this Special Issue.
Dr. Eneko Osaba
Dr. Diana Manjarres
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 papers will be 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. Processes is an international peer-reviewed open access monthly 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 2000 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.
- Bioinspired computation
- Optimization of energy systems
- Energy storage optimization
- Optimization modeling of energy systems planning
- Model-based decision support tools in sustainable energy
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: A Hybrid LSTM–Based Genetic Programming Approach for Short-Term Prediction of Global Solar Radiation Using Weather Data
Authors: Rami Alhajj
Affiliation: College of Engineering and Technology, American University of the Middle East, Kuwait
Abstract: The integration of Solar Energy in smart grids and many other utilities is continuously increasing due to its economic and environmental benefits. However, the uncertainty of available solar energy creates challenges regarding the stability of the generated power and consequently consistency of the supply-demand balance. An accurate global solar radiation short-term prediction model can resolve such type of problems to ensure an overall system reliability and power generation scheduling. This article describes a non-linear hybrid model based on Long Short-Term Memory recurrent models and genetic programming technique for short term prediction of global solar radiation. The LSTMs are recurrent neural models that are successfully used to model and predict time series data. We use these models as a base for the prediction of global solar radiation using measured weather data. Genetic programming is an evolutionary computing technique that enables automatic search for complex solution formulas. We use the Genetic Programing technique combined with the LSTM models to find the best continuous outputs that predict the global solar radiation on horizontals for a short time period ahead. A reference model to predict the global solar radiation is first presented, then, an enhanced hybrid approach is presented and analyzed. Statistical analysis measures are used to evaluate the performance of the proposed approach.