Analysis of time series forecasting in application to solar energy harvest

The promised future applications in solar energy harvest have been remarkably recognized. However, the hourly forecasting of normal solar irradiance (NSI) outputs is considered a problem due to the dynamic nature of meteorological information not only in a day but also across days. The thesis proposed three neural network models including a dense layer without a hidden layer (DNN_h0), a dense neural network with two hidden layers (DNN_h2), a dense neural network with two hidden layers associated with one intermediate metrological feature (air temperature: T) (DNN_h2T), and dense neural network with two hidden layers associated with 7 intermediate metrological features (DNN_h2F). These models would be used to forecast an hourly prediction of normal solar irradiance (NSI) across an entire day. As well as, we proposed two configurations to represent our datasets: FTC (sine-cosine) and 1H (one-hot) encodings. In addition, we used metrological features such as air temperature T and others to determine the effectiveness of a model’s performance in terms of mean absolute error (MAE). We conducted two groups of experiments: single-step and multi-step prediction models by using one real-world dataset (NREL). As a result, the comparison is revealed that the (NSI) has an acceptable model performance in both FTC and 1H encodings for the multi-step models by using an intermediate metrological feature: air temperature T in the (DNN_h2T) model. Whereas the single-step model (DNN_h0) has shown slightly acceptance to find a well performance to predict the (NSI), while the (DNN_h2) model shows a significant (MAE) values in both encodings

Evaluation and Design Exploration of Solar Harvested-Energy Prediction Algorithm

To respond to variations in solar energy, harvested-energy prediction is essential to harvested-energy management approaches. The effectiveness of such approaches is dependent on both the achievable accuracy and computation overhead of prediction algorithm implementation. This paper presents detailed evaluation of a recently reported solar energy prediction algorithm to determine empirical bounds on achievable accuracy and implementation overhead using an effective error evaluation technique. We evaluate the algorithm performance over varying prediction horizons and propose guidelines for algorithm parameter selection across different real solar energy profiles to simplify implementation. The prediction algorithm computation overhead is measured on actual hardware to demonstrate prediction accuracy-cost trade-off. Finally, we motivate the basis for dynamic prediction algorithm and show that more than 10% increase in prediction accuracy can be achieved compared to static algorithm

Evaluation and Design Exploration of Solar Harvested-Energy Prediction Algorithm

Abstract—To respond to variations in solar energy, harvestedenergy prediction is essential to harvested-energy management approaches. The effectiveness of such approaches is dependent on both the achievable accuracy and computation overhead of prediction algorithm implementation. This paper presents detailed evaluation of a recently reported solar energy prediction algorithm to determine empirical bounds on achievable accuracy and implementation overhead using an effective error evaluation technique. We evaluate the algorithm performance over varying prediction horizons and propose guidelines for algorithm parameter selection across different real solar energy profiles to simplify implementation. The prediction algorithm computation overhead is measured on actual hardware to demonstrate prediction accuracy-cost trade-off. Finally, we motivate the basis for dynamic prediction algorithm and show that more than 10% increase in prediction accuracy can be achieved compared to static algorithm. I

Contribution au domaine de la conception d’objets communicants embarqués basse consommation et autonomes en énergie

This report proposes a synthesis of my research and teaching activities. Since 2008, as associate professor at the University of Nice Sophia Antipolis, I did my research into the MCSOC team from the LEAT laboratory. For nearly 15 years, my activity is focused on the design of embedded communicating objects, with a strong emphasis for high level approach allowing, early in the design flow, to model and optimize the performance as well as the consumed energy. Those system-level approaches are more and more relevant over the last few years and become a must-have solution for designing efficient embedded systems. My activity on energy harvesting for autonomous systems brings an original contribution to this domain and has a national and international impact. This document is organized in two parts: the first part is a synthesis of my research and teaching activity, while the second one presents in details my research work, putting in evidence my contributions and innovative aspects. The manuscript ends with a scientific overview as well as some perspectives.Ce manuscrit présente une synthèse de mes travaux de recherche. Depuis septembre 2008, date de ma nomination en tant que Maître de Conférences à l’Université de Nice Sophia Antipolis, j’ai effectué mes travaux de recherche au sein de la thématique MCSOC (Modélisation, Conception Système d’Objets Communicants) du laboratoire LEAT (Université de Nice Sophia Antipolis, UMR CNRS 7248). Depuis maintenant près de 15 ans, mes travaux de recherche s’intéressent au domaine de la conception d’objets communicants embarqués avec une évolution forte vers des approches de haut niveau d’abstraction permettant tôt dans le flot de conception, de modéliser et d’optimiser les performances et la consommation d’énergie. Ces approches de niveau système n’ont cessé de prendre de l’ampleur ces dernières années et s’installent aujourd’hui comme une solution incontournable du domaine de la conception de systèmes embarqués. Mes travaux plus spécifiques sur l’autonomie énergétique de ces systèmes apportent une contribution originale au domaine et ont un rayonnement national et international. Ce document est organisé en deux parties : la première partie propose une synthèse des travaux de recherche et d’enseignement ; la seconde présente de manière détaillée mes travaux de recherche en mettant en avant toutes ses contributions et originalités. Le manuscrit s’achève par un bilan scientifique ainsi que quelques perspectives de recherche