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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "C: Energy Economics and Policy".

Deadline for manuscript submissions: closed (15 November 2019) | Viewed by 51618

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Special Issue Editors

Dr. Benedetto Nastasi

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Department of Planning, Design & Technology of Architecture, Sapienza University of Rome, Via Flaminia 72, 00196 Rome, Italy
Interests: building physics; building services engineering; building simulation; renewable energy technologies; indoor environmental quality; open data & energy analytics; energy efficiency; zero energy buildings; power-to-X solutions; buildings, district and national energy systems
Special Issues, Collections and Topics in MDPI journals

Dr. Massimiliano Manfren

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Guest Editor

Faculty of Engineering and the Environment, University of Southampton, Highfield, Southampton SO17 1BJ, UK
Interests: building physics; building services engineering; renewable energy technologies; data mining; operation research; analytics; sustainability transitions; energy transitions; open data; open science
Special Issues, Collections and Topics in MDPI journals

Dr. Michel Noussan

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Guest Editor

Future Energy Program (FEP), Fondazione Eni Enrico Mattei, Corso Magenta 63, 20123 Milan, Italy
Interests: energy systems; transport; renewable energy sources; data analysis; open data; energy statistics; decarbonization; digitalization
Special Issues, Collections and Topics in MDPI journals

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:

Open data and energy sustainability;
Open data science and energy planning;
Open science and open governance for sustainable development goals;
Key performance indicators of data-aware energy modelling, planning and policy;
Energy, water and sustainability database for building, district and regional systems;
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 submissions that pass pre-check are 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 2600 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 (11 papers)

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Editorial

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3 pages, 160 KiB

Open AccessEditorial

Open Data and Energy Analytics

by Benedetto Nastasi, Massimiliano Manfren and Michel Noussan

Energies 2020, 13(9), 2334; https://doi.org/10.3390/en13092334 - 07 May 2020

Cited by 7 | Viewed by 1989

Abstract

This pioneering 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. Ten high-quality papers were published after [...] Read more.

(This article belongs to the Special Issue Open Data and Energy Analytics)

Research

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15 pages, 478 KiB

Open AccessArticle

Energy Potential Mapping: Open Data in Support of Urban Transition Planning

by Michiel Fremouw, Annamaria Bagaini and Paolo De Pascali

Energies 2020, 13(5), 1264; https://doi.org/10.3390/en13051264 - 09 Mar 2020

Cited by 18 | Viewed by 2774

Abstract

Cities play a key role in driving the transition to sustainable energy. Urban areas represent between 60% and 80% of global energy consumption and are a significant source of CO₂ emissions, making energy management at the urban scale an important area of research. Urban energy systems have a strong influence on the environment, economy, social dimensions and urban spatial planning. Energy consumption affects the urban microclimate, urban comfort, human health, and conversely, urban physical, economic and social characteristics affect the energy urban profile. In order to improve the quality of energy strategies, policies, and plans, local authorities need decision support tools, like energy potential mapping, which have risen significance in the last decades. Energy data are crucial for those tools. They can increase the quality and effectiveness of energy planning but also support the integration between energy and spatial planning. Energy data can also stimulate citizen engagement as well as encourage sustainable behaviours and CO₂ emission reduction. This paper aims to increase the practice of data-aware planning, through the study of problems in energy data acquisition and processing observed in European projects focused on developing energy mapping tools. The problems observed attend to two main areas: technical and socio-economic issues. Those were derived from a comparison of energy mapping tools, and the work conducted for the PLANHEAT development. The scope of the research is to understand the main recurring issues in energy data acquisition and processing, in order to overcome the barriers in data availability. Increasing awareness of the relevance of energy data can foster the use of energy mapping tools, increasing the quality of energy policies and planning. Full article

(This article belongs to the Special Issue Open Data and Energy Analytics)

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14 pages, 2416 KiB

Open AccessArticle

Parametric Performance Analysis and Energy Model Calibration Workflow Integration—A Scalable Approach for Buildings

by Massimiliano Manfren and Benedetto Nastasi

Energies 2020, 13(3), 621; https://doi.org/10.3390/en13030621 - 01 Feb 2020

Cited by 25 | Viewed by 3479

Abstract

High efficiency paradigms and rigorous normative standards for new and existing buildings are fundamental components of sustainability and energy transitions strategies today. However, optimistic assumptions and simplifications are often considered in the design phase and, even when detailed simulation tools are used, the validation of simulation results remains an issue. Further, empirical evidences indicate that the gap between predicted and measured performance can be quite large owing to different types of errors made in the building life cycle phases. Consequently, the discrepancy between a priori performance assessment and a posteriori measured performance can hinder the development and diffusion of energy efficiency practices, especially considering the investment risk. The approach proposed in the research is rooted on the integration of parametric simulation techniques, adopted in the design phase, and inverse modelling techniques applied in Measurement and Verification (M&V) practice, i.e., model calibration, in the operation phase. The research focuses on the analysis of these technical aspects for a Passive House case study, showing an efficient and transparent way to link design and operation performance analysis, reducing effort in modelling and monitoring. The approach can be used to detect and highlight the impact of critical assumptions in the design phase as well as to guarantee the robustness of energy performance management in the operational phase, providing parametric performance boundaries to ease monitoring process and identification of insights in a simple, robust and scalable way. Full article

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25 pages, 7753 KiB

Open AccessArticle

Open Source Data for Gross Floor Area and Heat Demand Density on the Hectare Level for EU 28

by Andreas Müller, Marcus Hummel, Lukas Kranzl, Mostafa Fallahnejad and Richard Büchele

Energies 2019, 12(24), 4789; https://doi.org/10.3390/en12244789 - 16 Dec 2019

Cited by 23 | Viewed by 4711

Abstract

The planning of heating and cooling supply and demand is key to reaching climate and sustainability targets. At the same time, data for planning are scarce for many places in Europe. In this study, we developed an open source dataset of gross floor area and energy demand for space heating and hot water in residential and tertiary buildings at the hectare level for EU28 + Norway, Iceland, and Switzerland. This methodology is based on a top-down approach, starting from a consistent dataset at the country level (NUTS 0), breaking this down to the NUTS 3 level and further to the hectare level by means of a series of regional indicators. We compare this dataset with data from other sources for 20 places in Europe. This process shows that the data for some places fit well, while for others, large differences up to 45% occur. The discussion of these results shows that the other data sources used for this comparison are also subject to considerable uncertainties. A comparison of the developed data with maps based on municipal building stock data for three cities shows that the developed dataset systematically overestimates the gross floor area and heat demand in low density areas and vice versa. We conclude that these data are useful for strategic purposes on aggregated level of larger regions and municipalities. It is especially valuable in locations where no detailed data is available. For detailed planning of heating and cooling infrastructure, local data should be used instead. We believe our work contributes towards a transparent, open source dataset for heating and cooling planning that can be regularly updated and is easily accessible and usable for further research and planning activities. Full article

(This article belongs to the Special Issue Open Data and Energy Analytics)

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16 pages, 2550 KiB

Open AccessArticle

Enhancement of a Short-Term Forecasting Method Based on Clustering and kNN: Application to an Industrial Facility Powered by a Cogenerator

by Giulio Vialetto and Marco Noro

Energies 2019, 12(23), 4407; https://doi.org/10.3390/en12234407 - 20 Nov 2019

Cited by 10 | Viewed by 2790

Abstract

In recent years, collecting data is becoming easier and cheaper thanks to many improvements in information technology (IT). The connection of sensors to the internet is becoming cheaper and easier (for example, the internet of things, IOT), the cost of data storage and data processing is decreasing, meanwhile artificial intelligence and machine learning methods are under development and/or being introduced to create values using data. In this paper, a clustering approach for the short-term forecasting of energy demand in industrial facilities is presented. A model based on clustering and k-nearest neighbors (kNN) is proposed to analyze and forecast data, and the novelties on model parameters definition to improve its accuracy are presented. The model is then applied to an industrial facility (wood industry) with contemporaneous demand of electricity and heat. An analysis of the parameters and the results of the model is performed, showing a forecast of electricity demand with an error of 3%. Full article

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16 pages, 1938 KiB

Open AccessArticle

Assessment of the Space Heating and Domestic Hot Water Market in Europe—Open Data and Results

by Simon Pezzutto, Silvia Croce, Stefano Zambotti, Lukas Kranzl, Antonio Novelli and Pietro Zambelli

Energies 2019, 12(9), 1760; https://doi.org/10.3390/en12091760 - 09 May 2019

Cited by 43 | Viewed by 5801

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 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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12 pages, 1264 KiB

Open AccessArticle

d2ix: A Model Input-Data Management and Analysis Tool for MESSAGE_ix

by Thomas Zipperle and Clara Luisa Orthofer

Energies 2019, 12(8), 1483; https://doi.org/10.3390/en12081483 - 18 Apr 2019

Cited by 1 | Viewed by 3497

Abstract

Bottom-up integrated assessment models, like MESSAGE_ix, 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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36 pages, 5318 KiB

Open AccessArticle

The Role of Open Access Data in Geospatial Electrification Planning and the Achievement of SDG7. An OnSSET-Based Case Study for Malawi

by Alexandros Korkovelos, Babak Khavari, Andreas Sahlberg, Mark Howells and Christopher Arderne

Energies 2019, 12(7), 1395; https://doi.org/10.3390/en12071395 - 11 Apr 2019

Cited by 57 | Viewed by 14056

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 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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25 pages, 791 KiB

Open AccessArticle

Towards an Automated, Fast and Interpretable Estimation Model of Heating Energy Demand: A Data-Driven Approach Exploiting Building Energy Certificates

by Antonio Attanasio, Marco Savino Piscitelli, Silvia Chiusano, Alfonso Capozzoli and Tania Cerquitelli

Energies 2019, 12(7), 1273; https://doi.org/10.3390/en12071273 - 02 Apr 2019

Cited by 25 | Viewed by 3642

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 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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28 pages, 10115 KiB

Open AccessArticle

Automatic Processing of User-Generated Content for the Description of Energy-Consuming Activities at Individual and Group Level

by Roos De Kok, Andrea Mauri and Alessandro Bozzon

Energies 2019, 12(1), 15; https://doi.org/10.3390/en12010015 - 21 Dec 2018

Cited by 5 | Viewed by 4458

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 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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14 pages, 3555 KiB

Open AccessArticle

Evaluation of Energy Distribution Using Network Data Envelopment Analysis and Kohonen Self Organizing Maps

by Thiago Gomes Leal Ganhadeiro, Eliane Da Silva Christo, Lidia Angulo Meza, Kelly Alonso Costa and Danilo Pinto Moreira de Souza

Energies 2018, 11(10), 2677; https://doi.org/10.3390/en11102677 - 09 Oct 2018

Cited by 5 | Viewed by 2881

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 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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