Artificial Intelligence and Nature-Inspired Optimization on Integrative Capacity of Renewable Energy in the Western Balkan (original) (raw)

Predicting the Economic Impact of Using Renewable Energy by Modelling Through Artificial Intelligence Techniques

European Journal of Sustainable Development

Diversification of energy supplies is one of the main priorities of the energy policy of the developed countries. The major objective of this research is the model for predicting the economic impact of renewable energy using artificial intelligence techniques. This has been achieved by using the neural networks for the various issues related to renewable energy. The designed model consist in identification of those macroeconomic indicators that are required for the database creation, validation and testing in the view of obtaining the smallest error in the validation set for predicting the renewable energy impact upon the economy. The performance of the model was also revealed by comparing control graphs.

Renewable Energy Sources (RES) Utilization and Adaptation Technologies using Artificial Intelligence Expert Systems to Support and Secure RES Projects, Investments and Initiatives

DISTRES Conference on the promotion of distributed Renewable Energy Sources in the Mediterranean Region, 2009

Selecting a renewable energy oriented technology in order to support an investment plan or a geo-political energy analysis or policy, is a quite complex and multi-parametrical process. The technical and operational differentiation among the energy technologies, techniques and approaches is based on the geographical, social, technical, financial, environmental, economical and regional constraints. Successful energy investment decisions and initiatives are based on multi-criteria and multi-dimensional decisions through extensive analysis of rules and facts per case. Such objectives can be managed via artificial intelligence expert system technologies. Intelligent systems can take as input any parameters that can contribute towards the best possible technology selection under given constraints or requirements. Such systems can be applied for every energy investment, initiative, and project, involving experts, government, civilians and any type of land owner of ruler. This paper identifies a major problem in the RES utilization and adaptation area and presents an in depth analysis of RES investments support technologies that can contribute. The paper describes the integration of the disciplines of Energy and Information Technology through applications that will promote and disseminate the viability and success of RES investments and utilization especially in the Mediterranean region where RES are available in abundance than in any other European region.

The Impact of Artificial Intelligence on Renewable Energy Systems

NeuroQuantology, 2022

Efficient use of energy in the context of technology, particularly renewable energy in the public sector, is essential, as buildings are the largest energy users, particularly government facilities, such as educational, health, and government facilities, as well as other public institutions with a high frequency of usage. However, recent advancements in artificial intelligence within the context of Big Data have not been fully leveraged in this sector. Renewable Energy Systems are critical components of the Smart City. Integrating renewable energy sources is beneficial for resolving the supply of energy and demand concerns. Their correct scale is required to adapt to integrating renewable energy sources in the future and maintain a steady condition of energy consumption. Different algorithms are required to execute the consolidated renewable energy system to meet technological, economic, and size issues. These sections discuss an in-depth examination of various issues regarding power production for Smart Cities using Renewable Energy Systems. Particular difficulties related to the integration of various energy sources, the usage of smart grids for integration, and techniques for scaling renewable energy utilizing software and artificial intelligence algorithms.

Impact of renewable energy utilization and artificial intelligence in achieving sustainable development goals

Energy Reports, 2021

Many countries around the world are planning to reach 100% renewable energy (RE) use by 2050. In this context and due to the recent sharp increase in RE utilization in the global energy mix along with its progressive impact on the world energy sector, the evaluation and investigation of its effect on achieving sustainable development goals (SDGs) are not covered sufficiently. Here, we present an assessment of the emerging role of RE utilization and artificial intelligence (AI) toward achieving SDGs. A total of 17 SDGs were divided into three groups, namely, environment, society, and economy, as per the three key pillars of sustainable development. The RE has a positive impact toward achieving 75 targets across all SDGs by using an expert elicitation method-based consensus. However, it may negatively affect the accomplishment of the 27 targets. In addition, the AI can help the RE to enable the attainment of 42 out of 169 targets. With the current exponential growth of RE share and AI development together with addressing certain present limitations, this impact may cover additional targets in the future. Nevertheless, the present research foci neglect significant facets. The exponential growth of RE share and rapid evolution of AI need to be accompanied through the requisite regulatory insight and technology regulation to cover additional targets in the future.

Multicriteria assessment of renewable energy sources in Serbia

2021

The development of today's economies is inconceivable without energy. However, fossil fuel reserves are declining, climate change is accelerating and some changes in the energy sector are needed. Renewable energy sources are a potential solution for many scientists and practitioners. However, the planning and implementation of renewable energy projects requires consideration of a number of criteria, which is why multicriteria decision-making methods are often used to evaluate renewable energy sources/technologies. Goal of this paper is to evaluate four types of renewable energy sources (photovoltaic, hydro, biomass and wind energy) in Serbia. Analytical hierarchical process and seven criteria were applied. Based on the obtained results, hydro sources are ranked the best. Also, a sensitivity analysis was conducted to determine whether changes in the priority of criteria would cause changes in the range of alternatives. It was found that major changes in priorities are needed for ...

Adjustable Model of Renewable Energy Projects for Sustainable Development: A Case Study of the Nišava District in Serbia

MDPI, 2018

This paper explores and ranks the key performance indicators of multi-criteria decision-making in the process of selecting renewable energy sources (RES). Different categories of factors (e.g., political, legal, technological, economic and financial, sociocultural, and physical) are crucial for the analysis of such projects. In this paper, we apply the fuzzy analytic hierarchy process (fuzzy AHP) method-a mathematical method-in order to analyze the main criteria for such projects, which include the environment, the organizational management structure, project participants, and participants' relationship with the performance indicators. In order of ranking, the indicators are the following: time, costs, quality, monitoring the project's sustainability, user feedback, and users' health and safety. The aim of this paper is to point out the necessity of creating an adjustable model for renewable energy projects in order to proceed with the sustainable development of the southeast part of Serbia. This model should lead the creation process for such a project, with the aim of increasing its energy efficiency.

Modelling Energy Consumption of the Republic of Serbia using Linear Regression and Artificial Neural Network Technique

Tehnicki vjesnik - Technical Gazette, 2019

The objectives of the study are twofold. First, we aim to examine the most influential socioeconomic indicators to explain energy consumption in Republic of Serbia. The second objective is to develop models that are able to predict the future energy consumption in the Republic of Serbia. This could be the first important step towards proper energy management in the country. Several potential socioeconomic indicators are selected to be the independent variables. Regression analysis is conducted to select the most relevant independent variables as well as building the multiple linear regression (MLR) models. In addition, an artificial neural networks (ANN) model is developed as a comparison. Finally, the energy demand is projected to the year 2022. It is found that both models show the declining trend with respect to the current level of energy consumption.

Artificial Intelligence (AI) in Renewable Energy Systems: A Condensed Review of its Applications and Techniques

2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), 2021

This paper’s main objective is to examine the state of the art of artificial intelligence (AI) techniques and tools in power management, maintenance, and control of renewable energy systems (RES) and specifically to the solar power systems. The findings would allow researchers to innovate the current state of technologies and possibly use the standard and successful techniques in building AI-powered renewable energy systems, specifically for solar energy. Various peer-reviewed journal articles were examined to determine the condition and advancement of the AI techniques in the field of RES, specifically in solar power systems. Different theoretical and experimental AI techniques often used and reliable techniques determined were the Artificial Neural Network (ANN), Backpropagation Neural Network (BPNN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Genetic Algorithm (GA). These techniques are widely used in different types of solar predictions based on the findings of this review. However, ANN stood out as the best of these techniques. ANN’s specific advantages over its competition include short computing time, higher accuracy, and generalization capabilities over other modeling techniques. This would translate to cost efficiency over other modeling techniques.

A fuzzy expert system for country-wide planning of renewable energy production and distribution in Turkey

2008

Renewable Energy production is becoming more important day by day due to a rapid increase for reliable, sufficient, timely, and economic and environmentally energy production. Public awareness about environment has a great impact on choosing the renewable resources for the renewable based electricity production systems. The Commission on Environment of the Turkish Grand National Assembly approved the Kyoto Protocol on Global Warming in June 2008, which makes the importance of renewable energy one step more important for Turkey. When all the potential renewable energy sources of Turkey is considered, hydraulic and wind and solar energies can meet almost 1/3 of the total demand and a very high percentage of the electricity production in the country. However country-wide planning of production and distribution is vital just because the potential of renewable resources diversed among the geographical regions and according to the time of the year in Turkey. The current structure of Turke...