Soteris A. Kalogirou

• DSc
• Professor (Full) at Cyprus University of Technology

Introduction The current study's goal is to apply an integrated approach of retrofitting a typical building in Cyprus that was designed and constructed for the refugee settlements in the period 1975-1985. The existing building is retrofitted to a nearly zero-energy building. Methods This typical type of building examined represents approximately 1...

A R T I C L E I N F O Keywords: Thermal-responsive smart window Ammonia pressure powered Broadband solar spectrum management Building energy saving A B S T R A C T Thermal responsive windows are highly promising for the next-generation architecture for their self-powered solar transmittance. However, existing thermochromic techniques, include VO 2-...

In the past decades, spectrally selective windows, such near-infrared absorbing (NIR) glazings, low-e glazings, and various smart windows, have been widely studied, developed, and applied in different climatic conditions for building energy-saving purposes. One major pathway of the spectrally selective glazings to achieve energy savings is through...

Metallic nanoparticles exhibit localized surface plasmon resonance, which gifts them with enhanced solar energy absorption in a special band. With an adjustable plasma resonance band from the visible light to the infrared, silver nanorods (AgNRs) are potential candidates for energy saving application. In this research, the optical properties of AgN...

In this chapter, an introduction to artificial intelligence techniques, including machine learning, ensemble learning, and deep learning, is presented. Several basic and comprehensive examples of different machine learning algorithms (such as support vector machine, K-nearest neighbors, linear regression, logistic regression, decision tree, random...

This chapter covers four applications: the first application is about one-step ahead forecasting of the daily global horizontal irradiation (GHI), using machine learning methods. The second application is related to one-step ahead forecasting of in-plane solar irradiance using deep learning neural networks. The third application concerns the invest...

Currently, a huge number of photovoltaic plants have been installed worldwide, and these plants should be carefully protected and supervised continually to be safe and reliable during their working lifetime. Faults in photovoltaic plants can mainly affect the photovoltaic array output. This chapter intends to present various applications of machine...

This chapter aims to present real-time implementations of some methods, described in the previous chapters, by using microprocessors (e.g., Raspberry Pi), microcontrollers (e.g., Arduino), and reconfigurable circuits such as field programmable gate array (FPGA). First, the main programmable electronic devices used in this area are described, then a...

In this chapter, numerous examples of the application of machine learning and deep learning algorithms for short-term photovoltaic output power forecasting are presented and discussed. The first application is about forecasting one-step ahead photovoltaic output power, using machine learning algorithms; in this application, only historical data are...

This chapter is composed of two parts: the first part provides a short introduction to solar radiation, which play a very important role for solar energy systems, including photovoltaic systems. The main equations and models used for modeling and simulation of solar radiation components are presented in this section. Some examples of these applicat...

Currently, the number of distributed generation (DG) units is increasing rapidly, including renewable resources like solar energy, wind energy, and other clean sources. Thus, a flexible and reliable operation that keeps satisfactory levels of safety and quality is one of the challenges that emerge with DG integration. Control, optimal management, a...

The maximization of photovoltaic power is a crucial task, particularly under abnormal shading conditions for the creation of various problems such as hotspots, shading effects, dust accumulation, aging, and others. This chapter covers the application of optimization methods based on artificial intelligence techniques for maximizing the photovoltaic...

DESCRIPTION Artificial intelligence (AI) techniques including machine learning and deep learning algorithms have shown their capability in solving complex problems in different sectors such as, natural language processing, pattern recognition, forecasting, robotics, and other applications. Researchers working in the field of solar energy applicatio...

The photovoltaic (PV) array is the most sensible element in PV plants, which is subject to different type of faults and defects. Thus, to keep these plants working efficiently they should be monitored and protected carefully. Some faults if they are not detected and isolated promptly they may lead to hazardous risks. The diagnosis of PV systems is...

Offshore wind offers an excellent opportunity for domestic renewable energy production with a vast potential for future energy systems. Offshore wind resource assessment, however, can be challenging. Remote sensing data e.g., Synthetic Aperture Radar (SAR), provide high spatial resolution detailed information on the spatial variability of offshore...

The reduced electrical efficiency of PV modules caused by the increase of cell temperature, is a crucial issue for photovoltaic applications in buildings. Traditional solutions focus on passive cooling techniques to achieve heat regulation of PV modules, but cannot use effectively solar radiation not absorbed by solar cells. Therefore, in the curre...

This article presents some of the main findings from the SDEWES conferences of 2021 within the field of renewable energy. More specifically, results are summarized and contextualised within solar energy and thermal comfort, wind power resource assessment, and biogas and biomass resources and technology. The two last sections deal more broadly with...

In this work, hourly measurements of global solar irradiances obtained from a pyranometer and direct normal irradiances obtained from sunshine duration sensor are assessed through an extensive quality control procedure and statistical analysis on the measured and derived solar parameters for a semimountainous location using data from the last fie y...

In this paper the introduction of hydrogen to the current energy system of Cyprus is examinepd. Drivers and barriers towards hydrogen economy are identified and possible solutions are proposed in terms of both policy planning and infrastructure. The introduction of hydrogen to the current energy system of Cyprus will have numerous advantages in all...

Cyprus is often called the “sun island” because of the amount of sunshine received all year round. The abundance of solar radiation together with a good technological base, has created favourable conditions for the exploitation of solar energy on the island as Dr Soteris Kalogirou, Higher Technical Institute, Cyprus describes in this article.

The objective of this work is to train an artificial neural network (ANN) to learn to predict the required heating load of buildings with the minimum of input data. An ANN has been trained based on 250 known cases of heating load, varying from very small rooms (1-2 mP 2 P) to large spaces of 100 mP 2 P floor area. The type of rooms varied from smal...