Soteris A. Kalogirou

Bio: Soteris A. Kalogirou is an academic researcher from Cyprus University of Technology. The author has contributed to research in topics: Solar energy & Renewable energy. The author has an hindex of 72, co-authored 229 publications receiving 22731 citations. Previous affiliations of Soteris A. Kalogirou include Higher Technical Institute of Cyprus.

Artificial intelligence techniques for sizing photovoltaic systems: A review

Abstract: Artificial intelligence (AI) techniques are becoming useful as alternate approaches to conventional techniques or as components of integrated systems. They have been used to solve complicated practical problems in various areas and are becoming more and more popular nowadays. AI-techniques have the following features: can learn from examples; are fault tolerant in the sense that they are able to handle noisy and incomplete data; are able to deal with non-linear problems; and once trained can perform prediction and generalization at high speed. AI-based systems are being developed and deployed worldwide in a myriad of applications, mainly because of their symbolic reasoning, flexibility and explanation capabilities. AI have been used and applied in different sectors, such as engineering, economics, medicine, military, marine, etc. They have also been applied for modeling, identification, optimization, prediction, forecasting, and control of complex systems. The main objective of this paper is to present an overview of the AI-techniques for sizing photovoltaic (PV) systems: stand-alone PVs, grid-connected PV systems, PV-wind hybrid systems, etc. Published literature presented in this paper show the potential of AI as a design tool for the optimal sizing of PV systems. Additionally, the advantage of using an AI-based sizing of PV systems is that it provides good optimization, especially in isolated areas, where the weather data are not always available.

Fault detection and diagnosis methods for photovoltaic systems: A review

Abstract: Faults in any components (modules, connection lines, converters, inverters, etc.) of photovoltaic (PV) systems (stand-alone, grid-connected or hybrid PV systems) can seriously affect the efficiency, energy yield as well as the security and reliability of the entire PV plant, if not detected and corrected quickly. In addition, if some faults persist (e.g. arc fault, ground fault and line-to-line fault) they can lead to risk of fire. Fault detection and diagnosis (FDD) methods are indispensable for the system reliability, operation at high efficiency, and safety of the PV plant. In this paper, the types and causes of PV systems (PVS) failures are presented, then different methods proposed in literature for FDD of PVS are reviewed and discussed; particularly faults occurring in PV arrays (PVA). Special attention is paid to methods that can accurately detect, localise and classify possible faults occurring in a PVA. The advantages and limits of FDD methods in terms of feasibility, complexity, cost-effectiveness and generalisation capability for large-scale integration are highlighted. Based on the reviewed papers, challenges and recommendations for future research direction are also provided.

Role of renewable energy sources in environmental protection: A review

Abstract: Renewable technologies are considered as clean sources of energy and optimal use of these resources minimize environmental impacts, produce minimum secondary wastes and are sustainable based on current and future economic and social societal needs. Sun is the source of all energies. The primary forms of solar energy are heat and light. Sunlight and heat are transformed and absorbed by the environment in a multitude of ways. Some of these transformations result in renewable energy flows such as biomass and wind energy. Renewable energy technologies provide an excellent opportunity for mitigation of greenhouse gas emission and reducing global warming through substituting conventional energy sources. In this article a review has been done on scope of CO2 mitigation through solar cooker, water heater, dryer, biofuel, improved cookstoves and by hydrogen.

Artificial neural networks

Abstract: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

A review on the prediction of building energy consumption

Abstract: The energy performance in buildings is influenced by many factors, such as ambient weather conditions, building structure and characteristics, the operation of sub-level components like lighting and HVAC systems, occupancy and their behavior. This complex situation makes it very difficult to accurately implement the prediction of building energy consumption. This paper reviews recently developed models for solving this problem, which include elaborate and simplified engineering methods, statistical methods and artificial intelligence methods. Previous research work concerning these models and relevant applications are introduced. Based on the analysis of previous work, further prospects are proposed for additional research reference.