• Energy Research

Accurate prediction of building energy need plays a fundamental role in building design, despite the high computational cost to search for optimal energy saving solutions. An important advancement in the reduction of computational time could come from the application of machine learning models to circumvent energy simulations. With the goal of drastically limiting the number of simulations, in this paper we investigate the regression performance of different machine learning models, i.e., Support Vector Machine, Random Forest, and Extreme Gradient Boosting, trained on a small data-set of energy simulations performed on a case study building. Among the XX algorithms, the tree-based Extreme Gradient Boosting showed the best performance. Overall, we find that machine learning methods offer efficient and interpretable solutions, that could help academics and professionals in shaping better design strategies, informed by feature importance.

Optimal livestock production is a key contributor to the achievement of sustainable development goals. The management and disposal of livestock manure is one of the main issues facing the sector in terms of soil, water and air pollution. Proper and sustainable management of livestock manure also requires a systemic approach to the problem, considering it at different territorial levels. In order to identify existing strategies to support this issue, this review investigated the use of Geographic Information System (GIS) analysis as a support for livestock manure management, highlighting the several GIS methodologies used to provide insight into the complexity, power, and potential offered by these approaches in study areas with different economic, social, and environmental variables, and to provide insights for future research. The study was performed on 139 papers chosen from a literature screening. Three study themes were identified by co-word analysis: Bioenergy, Environmental pollution and Landscape management/development, with a percentage division of research articles of 38 %, 47 % and 15 %, respectively. This study provides a theoretical and prospective framework for the long-term expansion of the livestock sector, which is critical to promoting a balance between sector development and environmental impact. The use of spatial analysis, along with additional tools and methods such as modelling, multivariate and spatial statistics, life cycle assessment, machine learning and multi-criteria analysis, has proven to be widely applied.

The path towards nearly-Zero Energy Buildings has enforced stricter constraints in construction design while promoting the investigation of new architectural solutions, in residential and producing sectors. Energy simulations, integrated with machine learning, helps academics and professionals to investigate novel strategies for energy saving. We present here a 2-step methodology based on genetic algorithms, aiming to reduce the energy consumption for indoor heating and cooling, while identifying the most suitable commercial solutions for external wall and roof constructions. We compare it with a 1-step optimization algorithm with the goal to determine pros and cons of both methodologies. Even if the two methodologies are comparable in terms of energy reduction, the 2-step algorithm is less computationally expensive and finds several plausible architectural solutions, with equivalent energy profile.

Abstract Energy saving in production processes is a topical issue in wine sector, where energy demand is mostly due to temperature control. Thus constructive solutions and technological systems that maximize energy efficiency are requested by companies and stakeholders. Underground cellars may represent a solution, as they can provide temperature peaks dampening, thermal wave phase shifting and temperature variation breaking down. The effects depend on building and site features; therefore the knowledge of underground thermal properties and space-time variability of thermal phenomena are fundamental for building design. The objective of the study is to identify time variations of temperature distributions related to ground layers interacting with underground buildings in the wine sector. A monitoring system was implemented and experimental analyses were carried out on two boreholes at different distances from a real underground cellar. Results showed to what extent building design can take advantage of underground thermal properties to minimize energy demand for temperature control. The validity of the standard model of underground temperature distribution was verified for the shallow zone and a procedure was proposed and validated for the assessment of thermal diffusivity. The research represents also a basis for theoretical provisional model definition for surface zones.

Abstract. This study represents the first contribution within a research project aimed at defining design criteria for energy efficiency in food processing farm buildings. In this context, small to medium wine-growing and producing farms call for a specific approach to building design, since criteria available for industrial wineries are not suited to this production sector. This study assesses the effects of various building design solutions on the energy performances of farm wineries and estimates relevant differences in construction costs. The following design variables were considered: plan and elevation composition, sun-shading surfaces, and construction technology. The following building design solutions were analyzed and compared through 3D computer modeling, energy simulation, and finite element structural analysis: aboveground building (I), partially underground building (II), and partially underground building with sun-shading elements (III). The analyses provided results in terms of the variation of mean indoor temperature throughout the year, the amount of energy to be transferred to maintain predefined temperatures in areas provided with thermal control systems, and differences in construction costs. Based on these results, we found that the partially underground solution (II) produced a reduction in the overall energy demand of 14% for heating and 50% for cooling in comparison with the aboveground solution (I). In particular, concerning the wine aging room, if it is built totally underground, the reduction in the energy demand for this area is 100% for heating and 75% for cooling. The adoption of sun-shading devices (solution III) allows an overall reduction of 27% for cooling. Design solutions II and III entail construction cost increases ranging from 12% to 27%, depending on the construction technology, but also allow wineries to reduce their energy consumption by up to 63%, and consequently their operating costs, with positive effects on the farm’s image due to ever-increasing environmental concerns. This study also takes into consideration the effects of wine barrels on the aging room energy demands. Different numbers of barrels were introduced into the simulations, and results showed an energy demand increase of 12% to 26% to store 400 barrels and a linear dependence between the number of barrels and the energy demand increase.

Abstract Integrated energy planning (IEP) plays vital role in the promotion of energy efficiency in large-scale building stock. IEP can facilitate the evaluation of energy supply and demand in current rural and urban areas for the proper allocation of available resources. This paper presents a comprehensive review of large-scale energy planning and a systematic review of the methods employed in urban and regional integrated energy planning (UR-IEP). To this effect, 234 models from 157 published papers have been collected, classified based on their aims and methodologies, and critically subjected to meta-analysis and SWOT table. Thus, this review propones a framework of the fundamental concepts in energy model design and detailed analysis to support decision-making. Further, it provides a clear comparison of the methods and characterises them based on seven basic criteria of energy models, including purpose, methodology, analytical approach, geographical coverage, mathematical approach, time horizon, and data requirements. This framework provides urban planners with accurate and helpful basis for the selection of appropriate IEP methods based on the most common-focused methods published between 1960 and 2018.

Shallow Geothermal Systems (SGS) are widely used to provide low-cost heating and cooling of residential and commercial buildings. SGS can be an economically-viable solution even for commercial buildings, where controlled temperature is fundamental for the production processes. To assess the thermal resistance of the soil and the performance of a SGS, Thermal Response Tests (TRT) must be performed. TRT machines are today designed mainly for short term monitoring, for relatively deep SGS (up to 200 m) and for being used by expert operators. Lightweight, low-cost machines for both fast and long term, reliable and unattended TRT for very Shallow Geothermal Systems (vSGS) are not available today. This paper describes the design of a micro-TRT machine (mTRT) for vSGS, which is gaining interest in the civil engineering, environmental, energy and food chain sectors. The paper describes the features of the wireless monitoring system, the design choices to achieve the required accuracy and the software developed for adding remote control capability. Experimental validation in a real test field demonstrates the quality of measurements collected for analysing the TRT data.

Abstract This paper aims at indicating and certifying the implemented framework for forecasting buildings' energy demand of the city of Bologna, Italy. The method is developed through an automated calibration and is based on 7 known, physics-based building parameters and 6 unknown, and highly uncertain variables. The proposed method focuses on reducing computing time while keeping the accuracy of the output by narrowing the uncertainties in predicting unknown parameters. To accomplish this task, 11 archetypes are defined which are representatives of the buildings in a specific neighborhood in Bologna, Italy. For every defined archetype, the most informative unknown variables are recognized and the Gaussian Process (GP) is employed to emulate the variable-to-data map. A wide sampling of the GP outputs is then applied by No-U-Turn Sampler (NUTS). The methodology is validated for 1156 Italian urban buildings based on the city database. The level of evaluation metrics demonstrates no bias in the output of the long-term forecasting while it accelerated the prediction of building energy demand and calibration on the city scale. The method is flexible for application in other contexts and various available urban datasets.