Continuous Glucose Monitoring Systems (CGMSs) allow measuring the blood glycaemic value of a diabetic patient at a high sampling rate, producing considerable amount of data. This data can be effectively used by machine learning techniques to infer future values of the glycaemic concentration, allowing the early prevention of dangerous hyperglycaemic or hypoglycaemic states and a better optimisation of the diabetic treatment. Most of the approaches in literature learn a prediction model from the past samples of the same patient, which needs extensive calibrations and limits the usability of the system. In this work, we investigate prediction models trained on glucose signals of a large and heterogeneous cohort of patients and then applied to infer future glucose level values on a completely new patient. To achieve this purpose, we designed and compared two different types of solutions that were proved successful in many time-series prediction problems, based respectively on Non-Linear Autoregressive Neural Network (NAR) and on Long Short-Term Memory (LSTM) networks. These solutions were experimentally compared with three literature approaches, respectively based on Feed-Forward neural networks (FNN), autoregressive (AR) models, and Recurrent Neural Networks (RNN). While NAR obtained good prediction accuracy only for short-term predictions (i.e. with prediction horizon within 30 min), LSTM obtained extremely good performance both for short and long-term glucose level inference (60 min and more), overcoming all the other methods both in terms of correlation between measured and predicted glucose signal and in terms of clinical outcome.

In recent years, the contrast against energy waste and pollution has become mandatory and widely endorsed. Among the many actors at stake, the building sector energy management is one of the most critical. Indeed, buildings are responsible for $40\%$ of total energy consumption only in Europe, affecting more than a third of the total pollution produced. Therefore, energy control policies of buildings (for example, forecast-based policies such as Demand Response and Demand Side Management) play a decisive role in reducing energy waste. On these premises, this paper presents an innovative methodology based on Internet-of-Things (IoT) technology for smart building indoor air-temperature forecasting. In detail, our methodology exploits a specialized Non-linear Autoregressive neural network for short- and medium-term predictions, envisioning two different exploitation: (i) on realistic artificial data and (ii) on real data collected by IoT devices deployed in the building. For this purpose, we designed and optimized four neural models, focusing respectively on three characterizing rooms and on the whole building. Experimental results on both a simulated and a real sensors dataset demonstrate the prediction accuracy and robustness of our proposed models.

Due to the continuous increasing importance of renewable energy sources as an alternative to fossil fuels, to contrast air pollution and global warming, the prediction of Global Horizontal Irradiation (GHI), one of the main parameters determining solar energy production of photovoltaic systems, represents an attractive topic nowadays. Solar irradiance is determined by deterministic factors (i.e. the position of the sun) and stochastic factors (i.e. the presence of clouds). Since the stochastic element is difficult to model, this problem can benefit from machine learning techniques, like artificial neural networks. This work proposes a methodology to forecast GHI in short- (i.e. from 15 min to 60 min) and mid-term (i.e. from 60 to 120 min) time horizons. For this purpose, we designed, optimised and compared four neural network architectures for time-series forecasting, respectively based on: i) Non-Linear Autoregressive, ii) Feed-Forward, iii) Long Short-Term Memory and iv) Echo State Network. The original data-set, consisting of GHI values sampled every 15min, has been pre-processed by applying different filtering techniques. Our results analysis compares the performance of the proposed neural networks identifying the best in terms of error rate and forecast horizon. This analysis highlights that the clear-sky index results the preferred filtering technique by giving greatly improvements in data-set pre-processing, and Echo State Network gives best accuracy results.