0000000000133709
AUTHOR
A. D'amico
Building energy performance forecasting: A multiple linear regression approach
Abstract Different ways to evaluate the building energy balance can be found in literature, including comprehensive techniques, statistical and machine-learning methods and hybrid approaches. The identification of the most suitable approach is important to accelerate the preliminary energy assessment. In the first category, several numerical methods have been developed and implemented in specialised software using different mathematical languages. However, these tools require an expert user and a model calibration. The authors, in order to overcome these limitations, have developed an alternative, reliable linear regression model to determine building energy needs. Starting from a detailed …
Building energy demand assessment through heating degree days: the importance of a climatic dataset
Abstract The weather is one of the main factors to consider when designing a building because it represents the most important boundary condition to affect the dynamic behaviour of the building. In the literature, many studies use the degree day to predict building energy demand. However, linking the results obtained from a generic building simulation tool with defined degree days, will not give reliable energy evaluation. The goal of this study is to demonstrate that the assessment of building energy demand through the use of the degree day is correct only if the determination of the climate index is a function of the same weather data. The relationship between Heating Degree-Day and heati…
Convolutional Neural Network for Dust and Hotspot Classification in PV Modules
20th IEEE International Conference on Environment and Electrical Engineering, EEEIC 2020, online, 9 Jun 2020 - 12 Jun 2020; Energies : open-access journal of related scientific research, technology development and studies in policy and management 13(23), 6357 (2020). doi:10.3390/en13236357 special issue: "Special Issue "Selected Papers from 20 IEEE International Conference on Environment and Electrical Engineering (EEEIC 2020)" / Special Issue Editor: Prof. Dr. Rodolfo Araneo, Guest Editor"
Artificial Neural Networks to assess energy and environmental performance of buildings: An Italian case study
Abstract Approximately 40% of the European energy consumption and a large proportion of environmental impacts are related to the building sector. However, the selection of adequate and correct designs can provide considerable energy savings and reduce environmental impacts. To achieve this objective, a simultaneous energy and environmental assessment of a building's life cycle is necessary. To date, the resolution of this complex problem is entrusted to numerous software and calculation algorithms that are often complex to use. They involve long diagnosis phases and are characterised by the lack of a common language. Despite the efforts by the scientific community in the building sector, th…
Regression analysis to design a solar thermal collector for occasional use
Abstract Optimal design of a solar thermal system is necessary to minimize payback time and to diffuse renewable energy use for Domestic Hot Water production in residential areas. More accurate design is crucial in the case of seasonal or occasional use of the system; indeed, the standard criteria generally applied to a design system for continuous use, can lead to considerable over-sizing. To speed up the design phase and to help the planner in the identification of the best solution without any complex evaluation or long computational time, it would be interesting to have available a simpler method than the standard procedures, but one that is reliable and accurate for the evaluation of t…
Application of optimized artificial intelligence algorithm to evaluate the heating energy demand of non-residential buildings at European level
Abstract A reliable preliminary forecast of heating energy demand of a building by using a detailed dynamic simulation software typically requires an in-depth knowledge of the thermal balance, several input data and a very skilled user. The authors will describe how to use Artificial Neural Networks to predict the demand for thermal energy linked to the winter climatization of non-residential buildings. To train the neural network it was necessary to develop an accurate energy database that represents the basis of the training of a specific Artificial Neural Networks. Data came from detailed dynamic simulations performed in the TRNSYS environment. The models were built according to the stan…
Multi-Energy School System for Seasonal Use in the Mediterranean Area
School buildings represent an energy-consuming sector of real estate where different efficiency actions are necessary. The literature shows how the design of a multi-energy system offers numerous advantages, however, there are problems related to the integration of cogeneration units with renewable energy sources due to the low flexibility of the first one and the high degree of uncertainty of the latter. The authors provide an alternative solution through the analysis of a case study consisting of a multiple energy system in three Sicilian schools, focusing on the system&rsquo
Modelling and analysis of real-world wind turbine power curves: Assessing deviations from nominal curve by neural networks
Abstract The power curve of a wind turbine describes the generated power versus instantaneous wind speed. Assessing wind turbine performance under laboratory ideal conditions will always tend to be optimistic and rarely reflects how the turbine actually behaves in a real situation. Occasionally, some aerogenerators produce significantly different from nominal power curve, causing economic losses to the promoters of the investment. Our research aims to model actual wind turbine power curve and its variation from nominal power curve. The study was carried out in three different phases starting from wind speed and related power production data of a Senvion MM92 aero-generator with a rated powe…
Results of a literature review on methods for estimating buildings energy demand at district level
Abstract In the framework of distributed energy planning, evaluating reliable energy profiles of different sectors has a prominent role. At the same time, it is a quite challenging task, since the availability of actual energy profiles of buildings at the district level is not widespread. A survey of over 70 studies in scientific literature has been accomplished and a set of criteria has been defined for classifying the selected contributions based on the energy demand data features, source and/or estimation methods, highlighting the ones adopting hourly energy profiles. As final results, tables summarizing the main methods characteristics and a selection of studies providing directly useab…
Life Cycle Sustainability Assessment of a dish-Stirling Concentrating Solar Power Plant in the Mediterranean area
Abstract Among the different renewable energy sources, solar energy shows the highest exploitation potential to satisfy a substantial portion of the worlds’ future energy demand, guaranteeing at the same time lower emissions than conventional energy providers. Much of this potential is usable thanks to Concentrating Solar Power (CSP) technologies, of which the dish-Stirling concentrator is the most efficient. Nevertheless, the production and installation phases of the dish-Stirling technology can have an environmental impact which motivated the assessment of the plant in the three dimensions of sustainability (environmental, economic and social). The present publication evaluated an existin…
Multiple criteria assessment of methods for forecasting building thermal energy demand
Abstract Nowadays worldwide directives have focused the attention on improving energy efficiency in the building sector. The research of models able to predict the energy consumption from the first design and energy planning phase is conducted to improve building sustainability. Use of traditional forecasting tools for building thermal energy demand tends to encounter difficulties relevant to the amount of data required, implementation of the models, computational costs and inability to generalize the output. Therefore, many studies focused on the research and development of alternative resolution methods, but the choice of the most convenient is not clear and simple. Single comparison of s…
Numerical Assessment of Heating Energy Demand for Office Buildings in Italy
Abstract Buildings energy consumption depends on several parameters, such as climate, envelope typologies, occupant behavior, intended use, etc.; assessment of the energy performance of a building requires substantial input data describing constructions, environmental contexts, thermo-physical properties, geometry, control strategies and several other parameters influencing the thermal balance. In the last years, several numerical approaches dedicated to building simulation have been tested developing specialized software. On the other hand, simplified building models permit the evaluation of indoor conditions and heating/cooling loads with a good level of accuracy and without excessive com…
Modelling relationship among energy demand, climate and office building features: A cluster analysis at European level
Abstract More than one-third of the energy demand of industrialised countries is due to achieving acceptable conditions of thermal comfort and lighting in buildings. Energy demand in buildings depends on a combination of several parameters, such as climate, envelope typologies, occupant behaviour, and intended use. Indeed, assessing a building’s energy performance requires substantial input data describing constructions, environmental conditions, envelope thermo-physical properties, geometry, control strategies, and several other parameters. This has been a very active area of research in recent years, and several numerical approaches have been developed for building simulation; furthermore…
Evaluation of building heating loads with dimensional analysis: Application of the Buckingham π theorem
Abstract A detailed assessment of building energy performance requires a large amount of input data concerning building typology, environmental conditions, envelope thermophysical properties, geometry, control strategies, and several other parameters. Notwithstanding, the use of specialized software tools poses many challenges in regards to the retrieval of reliable and detailed information, setting a steep learning curve for engineers and energy managers. To speed up the preliminary assessment phase, it might be more convenient to resort to a simplified model that allows the evaluation of heating energy demand with a good level of accuracy and without excessive computational cost or user e…