Electricity use profiling and forecasting at microgrid level

Σκοπός αυτής της διπλωματικής εργασίας είναι η δημιουργία ενός ευέλικτου και εύκολα προσαρμόσιμου εργαλείου που θα εφαρμοστεί σε microgrids για την δημιουργία ενεργιακών προφίλ χρήσης ηλεκτρικής ενέργειας και για την πρόβλεψη φορτίου. Το αρθρωτό αυτό εργαλείο ονομάζεται Divinus και η αρχιτεκτονική του αποτελείται από πολλά διασυνδεδεμένα και καλά καθορισμένα στοιχεία, όπου το καθένα αλληλεπιδρά άμεσα με το άλλο. Οι τρεις πρώτοι δομικοί πυλώνες της πλατφόρμας είναι η βάση δεδομένων, στην οποία αποθηκεύονται όλες οι πληροφορίες, το Django framework στο οποίο υπάρχει ο πηγαίος κώδικας και τέλος ο ιστότοπος όπου εμφανίζονται όλα τα αποτελέσματα. Το επόμενο σύνολο στοιχείων δεν αφορά τόσο την δομική όσο την λειτουργική πλευρά του Divinus. Στα στοιχεία αυτά εμπεριέχονται διαδικασίες όπως είναι η συλλογή δεδομένων που θα αποθηκευτούν στη βάση, η δημιουργία ενεργειακών προφίλ χρήση που θα εκτελεστεί πάνω στα δεδομένα που συλλέγονται καθώς και η πρόβλεψη φορτίου για την οποία θα χρησιμοποιηθούν δεδομένα από τα ενεργειακά προφίλ χρήσης. Μέσω τον αυτοοργανωτικών χαρτών, που είναι ανταγωνιστικά δίκτυα που παρέχουν τοπολογική χαρτογράφηση στα εισαγόμενα δεδομένα, πραγματοποιούμε τη δημιουργία ενεργιακών προφίλ χρήσης ηλεκτρικής ενέργειας με βάση τα συλλεχθέντα δεδομένα από το 2010 έως το 2017 της περιοχής των Ψαχνών Ευβοίας του Τεχνολογικού Εκπαιδευτικού Ινστιτούτου Στερεάς Ελλάδας. Μόλις η χαρτογράφηση των δεδομένων αυτών είναι πλήρης τοποθετηθούν σε ομάδες βάσει των χαρακτηριστικών τους, η διαδικασία πρόβλεψης είναι σε θέση να ξεκινήσει. Η πρόβλεψη πραγματοποιείται με βάση τη μεθοδολογία machine learning και πιο συγκεκριμένα μέσω του αλγόριθμο k-neighbours. Από τις δοκιμές που έχουν πραγματοποιηθεί μέχρι τώρα, παρατηρούμαι ότι το Divinus έχει υψηλή ακρίβεια και μικρά σφάλματα. Πιο συγκεκριμένα, με βάση τις προβλέψεις που πραγματοποιήθηκαν για τις επόμενες πέντε ημέρες, τον επόμενο μήνα και τον επόμενο χρόνο, το μέσο σφάλμα δεν υπερβεί το 5% για τις επόμενες πέντε ημέρες, το 12% για τον επόμενο μήνα και το 16% για το επόμενο έτος. Ως εκ τούτου, στο στάδιο που βρίσκεται αυτήν την στιγμή το Divinus μπορούμε να πούμε ότι αποτελεί ένα πολύ ελπιδοφόρο εργαλείο που είναι πιθανό να χρησιμοποιηθεί τόσο για βραχυπρόθεσμες όσο και για μεσοπρόθεσμες προβλέψεις.The aim of this thesis is to create a flexible and easily customized tool applicable in microgrids to carry out electricity use profiling and forecasting. This modular tool is called Divinus and its architecture consists of several interconnected well-defined components where each one interacts directly with the other. Τhe first three structural pillars of the platform are its database where all the information is stored, the Django framework in which the code exists and finally the website where all the results are displayed. Τhe next set of components are not as structural as they are functional. Upon them is based the collection of data that will be saved in the database, the use profile that will be performed on the collected data and the load forecasting for which use profiling data will be used. Through the Self-Organizing Map, that are competing networks that provide topological mapping to the imported data, we perform the use profiling based on the collected data of Technological Institute of Sterea Ellada, Psachna campus from 2010 till 2017. As soon as the use profiling is complete and these data are placed in clusters based on their characteristics the forecasting process is able to begin. The forecasting is performed based on the machine learning methodology and more specifically with the k-neighbours algorithm. From the tests that have been carried out so far, we observed that Divinus has a high accuracy and low mean errors. More specifically based on forecasts made for the next five days, the next month and the next year the average error does not exceed 5% for the next five days, 12% for next month and 16% for the next year. Therefore, at the current stage of the tools is we are able to say that it is quite promising tool and that is likely to be used for both short-term and medium-term forecasts

Clinical, epidemiological and virological features of acute hepatitis B in Italy

Purpose To evaluate the association of hepatitis B virus (HBV) genotypes, basal core promoter (BCP)/precore (PC) and S gene mutations with the clinical-epidemiological characteristics of acute hepatitis B (AHB) in Italy. Methods During July 2005–January 2007, 103 symptomatic AHB patients were enrolled and prospectively followed up at 15 national hospitals. HBV genotypes, BCP/ PC and S gene variants were determined by nested-PCR and direct sequence analysis. Results Genotype D, A and F were detected in 49, 45 and 6 % of patients, respectively. BCP, PC, and BCP plus PC variants were found in 3.1, 11.3 and 7.2 % of patients, respectively. At enrollment, 68.3 % of patients were hepatitis B e antigen (HBeAg)-positive and 31.7 % HBeAg-negative. BCP/PC mutations were more common in HBeAg-negative than in HBeAg-positive patients (p < 0.0001). Compared to genotype D patients, those harboring non-D genotypes were more frequently males (p = 0.023), HBeAg-positive (p < 0.001), had higher bilirubin (p = 0.014) and viremia (p = 0.034) levels and less frequently carried BCP/PC mutations (p < 0.001). Non-D genotype patients more often were from Central Italy (p = 0.001) and reported risky sexual exposure (p = 0.021). Two patients had received vaccination before AHB: one harbored genotype F; the other showed a S gene mutation. Four patients developed fulminant AHB; mutations were found in 2 of 3 patients who underwent BCP/ PC sequencing. After a 6-month follow-up, only 2 (2.8 %) patients developed persistent infection. Conclusion AHB by non-D genotypes is increasing in Italy and is associated with risky sexual exposure. The ability of some genotypes to cause persistent and/or severe infection in Italy warrants larger studies for clarificatio

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

Abnormalities of sodium handling and of cardiovascular adaptations during high salt diet in patients with mild heart failure.

BACKGROUND: Sodium retention and hormonal activation are fundamental hallmarks in congestive heart failure. The present study was designed to assess the ability of patients with asymptomatic to mildly symptomatic heart failure and no signs or symptoms of congestion to excrete ingested sodium and to identify possible early abnormalities of hormonal and hemodynamic mechanisms related to sodium handling. METHODS AND RESULTS: The effects of a high salt diet (250 mEq/day for 6 days) on hemodynamics, salt-regulating hormones, and renal excretory response were investigated in a balanced study in 12 untreated patients with idiopathic or ischemic dilated cardiomyopathy and mild heart failure (NYHA class I-II, ejection fraction < 50%) (HF) and in 12 normal subjects, who had been previously maintained a 100 mEq/day NaCl diet. In normal subjects, high salt diet was associated with significant increases of echocardiographically measured left ventricular end-diastolic volume, ejection fraction, and stroke volume (all P < .001) and with a reduction of total peripheral resistance (P < .001). In addition, plasma atrial natriuretic factor (ANF) levels increased (P < .05), and plasma renin activity and aldosterone concentrations fell (both P < .001) in normals in response to salt excess. In HF patients, both left ventricular end-diastolic and end-systolic volumes increased in response to high salt diet, whereas ejection fraction and stroke volume failed to increase, and total peripheral resistance did not change during high salt diet. In addition, plasma ANF levels did not rise in HF in response to salt loading, whereas plasma renin activity and aldosterone concentrations were as much suppressed as in normals. Although urinary sodium excretions were not significantly different in the two groups, there was a small but systematic reduction of daily sodium excretion in HF, which resulted in a significantly higher cumulative sodium balance in HF than in normals during the high salt diet period (P < .001). CONCLUSIONS: These results show a reduced ability to excrete a sodium load and early abnormalities of cardiac and hemodynamic adaptations to salt excess in patients with mild heart failure and no signs or symptoms of congestion

Polyfunctional Type-1, -2, and -17 CD8+ T Cell Responses to Apoptotic Self-Antigens Correlate with the Chronic Evolution of Hepatitis C Virus Infection

Caspase-dependent cleavage of antigens associated with apoptotic cells plays a prominent role in the generation of CD8+ T cell responses in various infectious diseases. We found that the emergence of a large population of autoreactive CD8+ T effector cells specific for apoptotic T cell-associated self-epitopes exceeds the antiviral responses in patients with acute hepatitis C virus infection. Importantly, they endow mixed polyfunctional type-1, type-2 and type-17 responses and correlate with the chronic progression of infection. This evolution is related to the selection of autoreactive CD8+ T cells with higher T cell receptor avidity, whereas those with lower avidity undergo prompt contraction in patients who clear infection. These findings demonstrate a previously undescribed strict link between the emergence of high frequencies of mixed autoreactive CD8+ T cells producing a broad array of cytokines (IFN-γ, IL-17, IL-4, IL-2…) and the progression toward chronic disease in a human model of acute infection

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

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