TIDAL ENERGY: THE CASE OF EURIPUS’ STRAITS

The tidal energy potential of low current tidal and marine currents is investigated in this work. Existing data on the current velocity and sea level at the Euripus’ strait in Evia, Greece, is used to compute the energy yield based on contemporary turbine designs. Requirements, limitations and opportunities concerning the exploitation of low velocity tidal streams are discussed. The exploitation of tidal energy technology in conjunction with RES microgrids is proposed for coastal areas with abundance of sun and wind such as the Mediterranean islands

TIDAL ENERGY: THE CASE OF EURIPUS’ STRAITS

The tidal energy potential of low current tidal and marine currents is investigated in this work. Existing data on the current velocity and sea level at the Euripus’ strait in Evia, Greece, is used to compute the energy yield based on contemporary turbine designs. Requirements, limitations and opportunities concerning the exploitation of low velocity tidal streams are discussed. The exploitation of tidal energy technology in conjunction with RES microgrids is proposed for coastal areas with abundance of sun and wind such as the Mediterranean islands

GAMIFYING ENERGY USER PROFILES

Smartege is an educational application which aims in educating users in the basics of electrical energy consumption and production and engage them in energy saving behavior, techniques and technologies. This is accomplished through the virtual, and eventually actual, management of residential and office buildings equipped with virtual devices and renewable energy sources, with energy specifications borrowed from actual commercial devices, towards the ultimate target of transforming the buildings into net Zero Energy Buildings. Ultimately, Smartege is a gamified application targeting the behavior modification of the users. Its content development follows the persuasive model and uses cognitive learning for the educational component and game mechanics for user motivation and triggering

GAMIFYING ENERGY USER PROFILES

Smartege is an educational application which aims in educating users in the basics of electrical energy consumption and production and engage them in energy saving behavior, techniques and technologies. This is accomplished through the virtual, and eventually actual, management of residential and office buildings equipped with virtual devices and renewable energy sources, with energy specifications borrowed from actual commercial devices, towards the ultimate target of transforming the buildings into net Zero Energy Buildings. Ultimately, Smartege is a gamified application targeting the behavior modification of the users. Its content development follows the persuasive model and uses cognitive learning for the educational component and game mechanics for user motivation and triggering

Machine Learning Platform for Profiling and Forecasting at Microgrid Level

The shift towards distributed generation and microgrids has renewed the interest in forecasting algorithms and methods, which need to take into account the advances in information, metering and control technologies in order to address the challenges of forecasting problems. Technologies such as machine learning have been proven useful for short-term electricity load forecasting, especially for microgrids, as they can also take into account several types of historical data and can adapt to changes often encountered in small-scale systems and on a short time scale. In this paper, we present a flexible and easily customized modular toolbox, called Divinus, for electricity use profiling and forecasting in microgrids. Divinus may support a variety of machine learning algorithms for forecasting and profiling that can be used independently or combined. For demonstration purposes, we have implemented Self-Organizing Maps for profiling and k-Neighbors for forecasting. The testing of the platform was based on electricity consumption data of the Euripus campus of the National and Kapodistrian University of Athens in Evia, Greece, from January 2010 till March 2018. The tests that have been carried out so far show that the platform can be easily customized and the algorithms examined yield high accuracy and acceptable mean errors for the case of a university campus energy profile

Machine Learning Platform for Profiling and Forecasting at Microgrid Level

The shift towards distributed generation and microgrids has renewed the interest in forecasting algorithms and methods, which need to take into account the advances in information, metering and control technologies in order to address the challenges of forecasting problems. Technologies such as machine learning have been proven useful for short-term electricity load forecasting, especially for microgrids, as they can also take into account several types of historical data and can adapt to changes often encountered in small-scale systems and on a short time scale. In this paper, we present a flexible and easily customized modular toolbox, called Divinus, for electricity use profiling and forecasting in microgrids. Divinus may support a variety of machine learning algorithms for forecasting and profiling that can be used independently or combined. For demonstration purposes, we have implemented Self-Organizing Maps for profiling and k-Neighbors for forecasting. The testing of the platform was based on electricity consumption data of the Euripus campus of the National and Kapodistrian University of Athens in Evia, Greece, from January 2010 till March 2018. The tests that have been carried out so far show that the platform can be easily customized and the algorithms examined yield high accuracy and acceptable mean errors for the case of a university campus energy profile