Explorative Multidimensional Analysis for Energy Efficiency: DataViz versus Clustering Algorithms

Cottafava, Dario; Sonetti, Giulia; Gambino, Paolo; Tartaglino, Andrea

doi:10.3390/en11051312

Cited by 9 publications

(4 citation statements)

References 63 publications

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“…Moreover, some papers present information with just one type of chart in which the time-scale and variables change, and these can be presented in isolation or as a composition. Some examples of graphs used in these papers are Sankey diagrams [62], heatmaps [66], radar charts [53], parallel coordinates [63,71], scatter plots [78], and sunbursts [75].…”

Section: Types Of Visualization Used In Relation To the Goal Of The A...mentioning

confidence: 99%

“…Scatter plots are used when different variables must be related to one or more objectives [2,10,55]. It offers the possibility to identify patterns [78] or separate clusters [71] in search of anomalies and allows for the analysis of design performance according to different alternatives [54]. Histograms have proven useful when comparing hourly and daily consumption [55,62,64], as well as weekly and monthly variations [44,47,60,61].…”

Section: Types Of Visualization Used In Relation To Performance Indic...mentioning

confidence: 99%

“…Regarding heating and cooling consumptionthe most studied parameters in the field-sunburst charts, parallel coordinates, and chord diagrams are the common visualization types chosen to present annual data as an overview, while bar and line charts, heatmaps, histograms, and scatter plots are preferred when the aim is to understand behavior over shorter periods of time. Parallel coordinates, in most cases, show interrelated design variables and attributes [10,52,71], allowing one to identify the impact generated by the design alternatives in general consumption and achieve a "direct reading key" between input and output [63]. Fur-thermore, the use of the pie/donut chart and its variations is observed in the following cases: when showing the total consumption and its subdivisions by category, e.g., heating, cooling, lighting, and hot water [51]; when comparing consumption between spaces [1] and equipment [45]; and when monitoring [48,50] and predicting [61] minimum and maximum consumption, with the help of color differentiation.…”

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Building Energy Simulation and Monitoring: A Review of Graphical Data Representation

2022

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Data visualization has become relevant in the framework of the evolution of big data analysis. Being able to understand data collected in a dynamic, interactive, and personalized way allows for better decisions to be made when optimizing and improving performance. Although its importance is known, there is a gap in the research regarding its design, choice criteria, and uses in the field of building energy consumption. Therefore, this review discusses the state-of-the-art of visualization techniques used in the field of energy performance, in particular by considering two types of building analysis: simulation and monitoring. Likewise, data visualizations are categorized according to goals, level of detail and target users. Visualization tools published in the scientific literature, as well as those currently used in the IoT platforms and visualization software, were analyzed. This overview can be used as a starting point when choosing the most efficient data visualization for a specific type of building energy analysis.

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Section: Types Of Visualization Used In Relation To the Goal Of The A...mentioning

confidence: 99%

Section: Types Of Visualization Used In Relation To Performance Indic...mentioning

confidence: 99%

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Building Energy Simulation and Monitoring: A Review of Graphical Data Representation

2022

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“…Therefore, the development of efficient, robust and reliable control management system is crucial in order to achieve the goals in terms of buildings primary energy reduction. In this framework, Cottafava et al [186] proposed a novel tool to be used in the energy management of large building stocks, which is capable to detect anomalies due to abnormal energy use. Their approach is not based on complex thermodynamic dynamic simulations of the buildings and their HVAC systems.…”

Section: Building Efficiencymentioning

confidence: 99%

Recent Advances in the Analysis of Sustainable Energy Systems

et al. 2018

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EU energy policy is more and more promoting a resilient, efficient and sustainable energy system. Several agreements have been signed in the last few months that set ambitious goals in terms of energy efficiency and emission reductions and to reduce the energy consumption in buildings. These actions are expected to fulfill the goals negotiated at the Paris Agreement in 2015. The successful development of this ambitious energy policy needs to be supported by scientific knowledge: a huge effort must be made in order to develop more efficient energy conversion technologies based both on renewables and fossil fuels. Similarly, researchers are also expected to work on the integration of conventional and novel systems, also taking into account the needs for the management of the novel energy systems in terms of energy storage and devices management. Therefore, a multi-disciplinary approach is required in order to achieve these goals. To ensure that the scientists belonging to the different disciplines are aware of the scientific progress in the other research areas, specific Conferences are periodically organized. One of the most popular conferences in this area is the Sustainable Development of Energy, Water and Environment Systems (SDEWES) Series Conference. The 12th Sustainable Development of Energy, Water and Environment Systems Conference was recently held in Dubrovnik, Croatia. The present Special Issue of Energies, specifically dedicated to the 12th SDEWES Conference, is focused on five main fields: energy policy and energy efficiency in smart energy systems, polygeneration and district heating, advanced combustion techniques and fuels, biomass and building efficiency.

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Quantitative assessment of environmental impacts at the urban scale: the ecological footprint of a university campus

Genta

Favaro

Sonetti

et al. 2021

Environ Dev Sustain

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This paper explores the consumption-based ecological footprint method and its application aiming at a quantitative assessment of the sustainability of a university campus. The goal is to inform the planning decision-making process and evaluate the socio-technical solutions implemented in local urban settings for reducing energy consumption, decreasing environmental impacts and improving the quality of life of the campus' inhabitants. The case study taken for the analysis is the Politecnico di Torino, a Higher Education Institution (HEI) located in Northern Italy counting around 33,000 enrolled students in 2016. Data were collected from departments and administrative units of the Politecnico di Torino to identify the pressure exerted by the campus activities on the ecosystem during a reference year (2016). The study identified six main categories of consumption that were associated with their ecological footprint, i.e. the amount of land needed to produce the required resources and to absorb the generated waste, including CO2 emissions. Total footprint resulted in 6,200 gha: about half of the total city area, meaning that the campus would need a 310 times larger area to be self-sufficient. Normalizing this result with the number of students yields 0.19 gha/student. Transports had the highest share, with 49.4% out of the total campus impact, whereas energy covered 40.1%. Food, waste, land use and water counted, respectively, for 5.7%, 3.7%, 0.7% and 0.5%. This study presents the most comprehensive analysis to date of the environmental impact associated with an Italian HEI. This methodology and its implementation for the specific case of HEIs contribute to gain a better understanding of the overall impact of a university campus, as well as to create thresholds for comparative analysis, decision-making tools and policymaking to reduce the ecological footprint of the educational sector.

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Explorative Multidimensional Analysis for Energy Efficiency: DataViz versus Clustering Algorithms

Cited by 9 publications

References 63 publications

Building Energy Simulation and Monitoring: A Review of Graphical Data Representation

Building Energy Simulation and Monitoring: A Review of Graphical Data Representation

Recent Advances in the Analysis of Sustainable Energy Systems

Quantitative assessment of environmental impacts at the urban scale: the ecological footprint of a university campus

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