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Special Issue "Open Data and Energy Analytics"

A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: 1 July 2019

Special Issue Editors

Guest Editor
Dr. Benedetto Nastasi

Department of Architectural Engineering & Technology, TU Delft University of Technology, Julianalaan 134, 2628BX Delft, The Netherlands
E-Mail
Interests: Open Science; Data-aware Energy Transition Planning; Renewable Energy Data; Energy Data Infrastructures; Handling Renewable Energy Excess; Power-To-X Solutions; Buildings, District and National Energy Systems
Guest Editor
Dr. Massimiliano Manfren

Faculty of Engineering and the Environment, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
E-Mail
Interests: building physics; building services engineering; renewable energy technologies; data mining; operation research; analytics; sustainability transitions; energy transitions; open data; open science
Guest Editor
Dr. Michel Noussan

Department of Energy, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
E-Mail
Interests: energy systems; renewable energy sources; data analysis; open data; district heating; combined heat and power; energy statistics

Special Issue Information

Dear Colleagues,

Open data and policy implications coming from data-aware planning entail collection and pre- and post-processing as operations of primary interest. Before these steps, making data available to people and their decision-makers is a crucial point. Referring to the relationship between data and energy, public administrations, governments and research bodies are promoting the construction of reliable and robust datasets to pursue policies coherent with the Sustainable Development Goals, as well as to allow citizens to make informed choices.

Energy engineers and planners must provide the simplest and most robust tools to collect, process and analyze data in order to offer solid data-based evidence for future projections in building, district and regional systems planning.

This Special Issue aims at providing the state-of-the-art on open energy data analytics, its availability in the different contexts, i.e., country peculiarities, and at different scales, i.e., building, district and regional for data-aware planning and policy-making.

For all the aforementioned reasons, we encourage researchers to share their original works on the field of open data and energy analytics. Topics of primary interest include, but are not limited to:

  1. Open data and energy sustainability;
  2. Open data science and energy planning;
  3. Open science and open governance for sustainable development goals;
  4. Key performance indicators of data-aware energy modelling, planning and policy;
  5. Energy, water and sustainability database for building, district and regional systems;
  6. Best practices and case studies.

Dr. Benedetto Nastasi
Dr. Massimiliano Manfren
Dr. Michel Noussan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • open data
  • energy planning
  • smart cities
  • open energy governance
  • data analytics
  • databases for urban dynamics

Published Papers (6 papers)

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Research

Open AccessArticle
Assessment of the Space Heating and Domestic Hot Water Market in Europe—Open Data and Results
Energies 2019, 12(9), 1760; https://doi.org/10.3390/en12091760
Received: 13 April 2019 / Revised: 30 April 2019 / Accepted: 8 May 2019 / Published: 9 May 2019
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Abstract
The paper investigates the European space heating (SH) and domestic hot water (DHW) market in order to close knowledge gaps concerning its size. The stimulus for this research arises from incongruences found in SH and DHW market’s data in spite of over two [...] Read more.
The paper investigates the European space heating (SH) and domestic hot water (DHW) market in order to close knowledge gaps concerning its size. The stimulus for this research arises from incongruences found in SH and DHW market’s data in spite of over two decades of scientific research. The given investigation has been carried out in the framework of the Hotmaps project (Horizon 2020—H2020), which aims at designing an open source toolbox to support urban planners, energy agencies, and public authorities in heating and cooling (H&C) planning on country, regional, and local levels. Our research collects and analyzes SH and DHW market data in the European Union (EU), specifically the amount of operative units, installed capacities, energy efficiency coefficients as well as equivalent full-load hours per equipment type and country, with a bottom-up approach. The analysis indicates that SH and DHW account for a significant portion of the total EU energy utilization (more than 20%), amounting to almost 3900 TWh/y. At the same time, the energy consumption provided by district heating (DH) systems exceeds the one of condensing boilers. While DH systems applications are growing throughout the EU, the replacement of elderly, conventional boilers progresses at a slower pace. Full article
(This article belongs to the Special Issue Open Data and Energy Analytics)
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Open AccessArticle
d2ix: A Model Input-Data Management and Analysis Tool for MESSAGEix
Energies 2019, 12(8), 1483; https://doi.org/10.3390/en12081483
Received: 7 March 2019 / Revised: 9 April 2019 / Accepted: 15 April 2019 / Published: 18 April 2019
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Abstract
Bottom-up integrated assessment models, like MESSAGEix, depend on the description of the capabilities and limitations of technological, economical and ecological parameters, and their development over long-time horizons. Even small models of a few nodes, technologies and model years require input-data sets [...] Read more.
Bottom-up integrated assessment models, like MESSAGEix, depend on the description of the capabilities and limitations of technological, economical and ecological parameters, and their development over long-time horizons. Even small models of a few nodes, technologies and model years require input-data sets involving several hundred thousand data points. Such data sets quickly become incomprehensible, which makes error detection, collaborative working and the interpretation of results challenging, especially for non-self-created models. In response to the resulting need for manageable, comprehensible, and traceable representation of input-data, we developed a Python-based spreadsheet interface (d2ix) that enables presentation and editing of model input-data in a concise form. By increasing accessibility and transparency of the model input-data, d2ix reduces barriers to entry for new modellers and simplifies collaborative working. This paper describes the methodology and introduces the open-source Python-package d2ix. The package is available under the Apache License, Version 2.0 on GitHub. Full article
(This article belongs to the Special Issue Open Data and Energy Analytics)
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Open AccessArticle
The Role of Open Access Data in Geospatial Electrification Planning and the Achievement of SDG7. An OnSSET-Based Case Study for Malawi
Energies 2019, 12(7), 1395; https://doi.org/10.3390/en12071395
Received: 10 March 2019 / Revised: 4 April 2019 / Accepted: 8 April 2019 / Published: 11 April 2019
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Abstract
Achieving universal access to electricity is a development challenge many countries are currently battling with. The advancement of information technology has, among others, vastly improved the availability of geographic data and information. That, in turn, has had a considerable impact on tracking progress [...] Read more.
Achieving universal access to electricity is a development challenge many countries are currently battling with. The advancement of information technology has, among others, vastly improved the availability of geographic data and information. That, in turn, has had a considerable impact on tracking progress as well as better informing decision making in the field of electrification. This paper provides an overview of open access geospatial data and GIS based electrification models aiming to support SDG7, while discussing their role in answering difficult policy questions. Upon those, an updated version of the Open Source Spatial Electrification Toolkit (OnSSET-2018) is introduced and tested against the case study of Malawi. At a cost of $1.83 billion the baseline scenario indicates that off-grid PV is the least cost electrification option for 67.4% Malawians, while grid extension can connect about 32.6% of population in 2030. Sensitivity analysis however, indicates that the electricity demand projection determines significantly both the least cost technology mix and the investment required, with the latter ranging between $1.65–7.78 billion. Full article
(This article belongs to the Special Issue Open Data and Energy Analytics)
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Open AccessArticle
Towards an Automated, Fast and Interpretable Estimation Model of Heating Energy Demand: A Data-Driven Approach Exploiting Building Energy Certificates
Energies 2019, 12(7), 1273; https://doi.org/10.3390/en12071273
Received: 22 February 2019 / Revised: 26 March 2019 / Accepted: 27 March 2019 / Published: 2 April 2019
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Abstract
Energy performance certification is an important tool for the assessment and improvement of energy efficiency in buildings. In this context, estimating building energy demand also in a quick and reliable way, for different combinations of building features, is a key issue for architects [...] Read more.
Energy performance certification is an important tool for the assessment and improvement of energy efficiency in buildings. In this context, estimating building energy demand also in a quick and reliable way, for different combinations of building features, is a key issue for architects and engineers who wish, for example, to benchmark the performance of a stock of buildings or optimise a refurbishment strategy. This paper proposes a methodology for (i) the automatic estimation of the building Primary Energy Demand for space heating ( P E D h ) and (ii) the characterization of the relationship between the P E D h value and the main building features reported by Energy Performance Certificates (EPCs). The proposed methodology relies on a two-layer approach and was developed on a database of almost 90,000 EPCs of flats in the Piedmont region of Italy. First, the classification layer estimates the segment of energy demand for a flat. Then, the regression layer estimates the P E D h value for the same flat. A different regression model is built for each segment of energy demand. Four different machine learning algorithms (Decision Tree, Support Vector Machine, Random Forest, Artificial Neural Network) are used and compared in both layers. Compared to the current state-of-the-art, this paper brings a contribution in the use of data mining techniques for the asset rating of building performance, introducing a novel approach based on the use of independent data-driven models. Such configuration makes the methodology flexible and adaptable to different EPCs datasets. Experimental results demonstrate that the proposed methodology can estimate the energy demand with reasonable errors, using a small set of building features. Moreover, the use of Decision Tree algorithm enables a concise interpretation of the quantitative rules used for the estimation of the energy demand. The methodology can be useful during both designing and refurbishment of buildings, to quickly estimate the expected building energy demand and set credible targets for improving performance. Full article
(This article belongs to the Special Issue Open Data and Energy Analytics)
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Open AccessArticle
Automatic Processing of User-Generated Content for the Description of Energy-Consuming Activities at Individual and Group Level
Energies 2019, 12(1), 15; https://doi.org/10.3390/en12010015
Received: 31 October 2018 / Revised: 17 December 2018 / Accepted: 18 December 2018 / Published: 21 December 2018
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Abstract
Understanding and improving the energy consumption behavior of individuals is considered a powerful approach to improve energy conservation and stimulate energy efficiency. To motivate people to change their energy consumption behavior, we need to have a thorough understanding of which energy-consuming activities they [...] Read more.
Understanding and improving the energy consumption behavior of individuals is considered a powerful approach to improve energy conservation and stimulate energy efficiency. To motivate people to change their energy consumption behavior, we need to have a thorough understanding of which energy-consuming activities they perform and how these are performed. Traditional sources of information about energy consumption, such as smart sensor devices and surveys, can be costly to set up, may lack contextual information, have infrequent updates, or are not publicly accessible. In this paper, we propose to use social media as a complementary source of information for understanding energy-consuming activities. A huge amount of social media posts are generated by hundreds of millions of people every day, they are publicly available, and provide real-time data often tagged to space and time. We design an ontology to get a better understanding of the energy-consuming activities domain and develop a text and image processing pipeline to extract from social media the description of energy-consuming activities. We run a case study on Istanbul and Amsterdam. We highlight the strength and weakness of our approach, showing that social media data has the potential to be a complementary source of information for describing energy-consuming activities. Full article
(This article belongs to the Special Issue Open Data and Energy Analytics)
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Open AccessArticle
Evaluation of Energy Distribution Using Network Data Envelopment Analysis and Kohonen Self Organizing Maps
Energies 2018, 11(10), 2677; https://doi.org/10.3390/en11102677
Received: 1 August 2018 / Revised: 30 August 2018 / Accepted: 5 October 2018 / Published: 9 October 2018
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Abstract
This article presents an alternative way of evaluating the efficiency of the electric distribution companies in Brazil. This assessment is currently performed and designed by the National Electric Energy Agency (ANEEL), a Brazilian regulatory agency, to regulate energy prices. This involves calculating the [...] Read more.
This article presents an alternative way of evaluating the efficiency of the electric distribution companies in Brazil. This assessment is currently performed and designed by the National Electric Energy Agency (ANEEL), a Brazilian regulatory agency, to regulate energy prices. This involves calculating the X-factor, which represents the efficiency evolution in the price-cap regulation model. The proposed model aims to use a network Data Envelopment Analysis (DEA) model with the network dimension as an intermediate variable and to use Kohonen Self-Organizing Maps (SOM) to correct the difficulties presented by environmental variables. In order to find which environmental variables influence the efficiency, factor analysis was used to reduce the dimensionality of the model. The analysis still uses multiple regression with the previous efficiency as the dependent variable and the four factors extracted from factor analysis as independent variables. The SOM generated four clusters based on the environment and the efficiency for each distributor in each group. This allows for a better evaluation of the correction in the X-factor, since it can be conducted inside each cluster with a maintained margin for comparison. It is expected that the use of this model will reduce the margin of questioning by distributors about the evaluation. Full article
(This article belongs to the Special Issue Open Data and Energy Analytics)
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