• Energy Research

Forecasting future heat load in smart district heating networks is a key problem for utility companies that need such predictions for optimizing their operational activities. From the statistical learning viewpoint, this problem is also very interesting because it requires to integrate multiple time series about weather and social factors into a dynamical model, and to generate models able to explain the relationships between weather/social factors and heat load. Typical questions in this context are: “Which variables are more informative for the prediction?” and “Do variables have different influence in different contexts (e.g., time instant or situations)?” We propose a methodology for generating simple and interpretable models for heat load forecasting, then we apply this methodology to a real dataset, and, finally, provide new insight about this application domain. The methodology merges multi-equation multivariate linear regression and forward variable selection. We generate a (sparse) equation for each pair day-of-the-week/hour-of-the-day (for instance, one equation concerns predictions of Monday at 0.00, another predictions of Monday at 1.00, and so on). These equations are simple to explain because they locally approximate the prediction problem in specific times of day/week. Variable selection is a key contribution of this work. It provides a reduction of the prediction error of 2.4% and a decrease of the number of parameters of 49.8% compared to state-of-the-art models. Interestingly, different variables are selected in different equations (i.e., times of the day/week), showing that weather and social factors, and autoregressive variables with different delays, differently influence heat predictions in different times of the day/week.

District Heating networks (DHNs) are promising technologies for heat distribution in residential and commercial buildings since they enable high efficiency and low emissions. Within the recently proposed paradigm of smart grids, DHNs have acquired intelligent tools able to enhance their efficiency. Among these tools, there are demand forecasting technologies that enable improved planning of heat production and power station maintenance. In this work we propose a comparative study for heat load forecasting methods on a real case study based on a dataset provided by an Italian utility company. We trained and tested three kinds of models, namely non-autoregressive, autoregressive and hybrid models, on the available dataset of heat load and meteorological variables. The optimal model, in terms of root mean squared error of prediction, was selected. It considers the day of the week, the hour of the day, some meteorological variables, past heat loads and social components, such as holidays. Results show that the selected model is able to achieve accurate 48-hours predictions of the heat load in several conditions (e.g., different days of the week, different times, holidays and workdays). Moreover, an analysis of the parameters of the selected models enabled to identify a few informative variables.

AbstractAtmospheric aerosols and their effect on clouds are thought to be important for anthropogenic radiative forcing of the climate, yet remain poorly understood1. Globally, around half of cloud condensation nuclei originate from nucleation of atmospheric vapours2. It is thought that sulfuric acid is essential to initiate most particle formation in the atmosphere3,4, and that ions have a relatively minor role5. Some laboratory studies, however, have reported organic particle formation without the intentional addition of sulfuric acid, although contamination could not be excluded6,7. Here we present evidence for the formation of aerosol particles from highly oxidized biogenic vapours in the absence of sulfuric acid in a large chamber under atmospheric conditions. The highly oxygenated molecules (HOMs) are produced by ozonolysis of α-pinene. We find that ions from Galactic cosmic rays increase the nucleation rate by one to two orders of magnitude compared with neutral nucleation. Our experimental findings are supported by quantum chemical calculations of the cluster binding energies of representative HOMs. Ion-induced nucleation of pure organic particles constitutes a potentially widespread source of aerosol particles in terrestrial environments with low sulfuric acid pollution.