Open data and energy analytics - An analysis of essential information for energy system planning, design and operation

Manfren, Massimiliano; Nastasi, Benedetto; Groppi, Daniele; Garcia, Davide Astiaso

doi:10.1016/j.energy.2020.118803

Cited by 56 publications

(32 citation statements)

References 178 publications

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“…Buildings are complex systems because their energy use and indoor conditions vary dynamically under the influence of weather, occupancy and component performance [19]. The drive towards a more sustainable built environment and low carbon transition of the energy system give rise to challenges that can only be met by multi-disciplinary knowledge [20,21]. Interaction of open data and models may become fundamental for monitoring, verifying and tracking performance at multiple levels [21].…”

Section: Identifying Suitable Open Geospatial Datasets and Previous Workmentioning

confidence: 99%

Utilising Open Geospatial Data to Refine Weather Variables for Building Energy Performance Evaluation—Incident Solar Radiation and Wind-Driven Infiltration Modelling

Skeie

Gustavsen

2021

Energies

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In building thermal energy characterisation, the relevance of proper modelling of the effects caused by solar radiation, temperature and wind is seen as a critical factor. Open geospatial datasets are growing in diversity, easing access to meteorological data and other relevant information that can be used for building energy modelling. However, the application of geospatial techniques combining multiple open datasets is not yet common in the often scripted workflows of data-driven building thermal performance characterisation. We present a method for processing time-series from climate reanalysis and satellite-derived solar irradiance services, by implementing land-use, and elevation raster maps served in an elevation profile web-service. The article describes a methodology to: (1) adapt gridded weather data to four case-building sites in Europe; (2) calculate the incident solar radiation on the building facades; (3) estimate wind and temperature-dependent infiltration using a single-zone infiltration model and (4) including separating and evaluating the sheltering effect of buildings and trees in the vicinity, based on building footprints. Calculations of solar radiation, surface wind and air infiltration potential are done using validated models published in the scientific literature. We found that using scripting tools to automate geoprocessing tasks is widespread, and implementing such techniques in conjunction with an elevation profile web service made it possible to utilise information from open geospatial data surrounding a building site effectively. We expect that the modelling approach could be further improved, including diffuse-shading methods and evaluating other wind shelter methods for urban settings.

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Section: Identifying Suitable Open Geospatial Datasets and Previous Workmentioning

confidence: 99%

Utilising Open Geospatial Data to Refine Weather Variables for Building Energy Performance Evaluation—Incident Solar Radiation and Wind-Driven Infiltration Modelling

Skeie

Gustavsen

2021

Energies

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“…The non-geometric information is typically determined based on the archetype/prototype buildings organized by building use, year of construction, climate zone, and building size [9]. Archetype buildings represent the common typologies and technologies in the building stock [10]. Building use information is essential for identifying archetype buildings for UBEM [11].…”

Section: Introductionmentioning

confidence: 99%

Integrating GIS-Based Point of Interest and Community Boundary Datasets for Urban Building Energy Modeling

et al. 2021

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“…The choice of the appropriate model, either in engineering problems or in any other problem, is also an important factor in addition to data [9,10]. In this sense, public and research organisms are increasingly promoting the use of open and robust datasets to boost policies coherent with the environmental exploitation, protection and conservation, as well as modelling tools available to the scientific community and decision-makers [11]. Although Soft Computing (SC) and Machine Learning (ML) techniques have the ability to handle uncertainty in data and are extensively used for modelling purposes, the real challenge in modelling studies is due to the inadequacy of data, since the adequacy of the models depends mainly on the the quality of the information used, so that, if a researcher does not have quality data, there will be no quality models.…”

Section: Introductionmentioning

confidence: 99%

Building Suitable Datasets for Soft Computing and Machine Learning Techniques from Meteorological Data Integration: A Case Study for Predicting Significant Wave Height and Energy Flux

et al. 2021

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Meteorological data are extensively used to perform environmental learning. Soft Computing (SC) and Machine Learning (ML) techniques represent a valuable support in many research areas, but require datasets containing information related to the topic under study. Such datasets are not always available in an appropriate format and its preparation and pre-processing implies a lot of time and effort by researchers. This paper presents a novel software tool with a user-friendly GUI to create datasets by means of management and data integration of meteorological observations from two data sources: the National Data Buoy Center and the National Centers for Environmental Prediction and for Atmospheric Research Reanalysis Project. Such datasets can be created using buoys and reanalysis data through customisable procedures, in terms of temporal resolution, predictive and objective variables, and can be used by SC and ML methodologies for prediction tasks (classification or regression). The objective is providing the research community with an automated and versatile system for the casuistry that entails well-formed and quality data integration, potentially leading to better prediction models. The software tool can be used as a supporting tool for coastal and ocean engineering applications, sustainable energy production, or environmental modelling; as well as for decision-making in the design and building of coastal protection structures, marine transport, ocean energy converters, and well-planned running of offshore and coastal engineering activities. Finally, to illustrate the applicability of the proposed tool, a case study to classify waves depending on their significant height and to predict energy flux in the Gulf of Alaska is presented.

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Open data and energy analytics - An analysis of essential information for energy system planning, design and operation

Cited by 56 publications

References 178 publications

Utilising Open Geospatial Data to Refine Weather Variables for Building Energy Performance Evaluation—Incident Solar Radiation and Wind-Driven Infiltration Modelling

Utilising Open Geospatial Data to Refine Weather Variables for Building Energy Performance Evaluation—Incident Solar Radiation and Wind-Driven Infiltration Modelling

Integrating GIS-Based Point of Interest and Community Boundary Datasets for Urban Building Energy Modeling

Building Suitable Datasets for Soft Computing and Machine Learning Techniques from Meteorological Data Integration: A Case Study for Predicting Significant Wave Height and Energy Flux

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