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Monitoring and Assessment of Energy Consumption through Remote Sensing

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 25136

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Special Issue Editor

Dr. Gregory Dobler

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Guest Editor

Biden School of Public Policy and Administration, Department of Physics and Astronomy and Data Science Institute, University of Delaware, Newark, DE 19716, USA
Interests: urban data science; cities as complex systems; observational data analysis techniques; energy consumption; air quality; lighting technology; public health; sustainability

Special Issue Information

Dear Colleagues,

The consumption of energy from local to global scales and the byproducts of that use have profound consequences for resource consumption, the climate, and social and environmental justice. Furthermore, the world is urbanizing, generating an imperative to understand the impacts of density, land use, and behavioral dynamics on energy use. At the same time, there are significant energy challenges outside of large cities. Rural communities have energy deficits, a high cost of distribution, and a lack of infrastructure, and numerous countries in the developing world have unstable distribution grids, rolling blackouts, and load shedding.

Metering data are increasingly being made available to the public and researchers, which has enhanced our understanding of energy use in larger urban systems; however, the temporal and spatial granularity of the publicly available data sets are (generally) insufficient to build models of demand. Moreover, it is a rarity to have any data at all, the vast majority of the world’s population is under-metered and has infrastructure that cannot support modern smart metering technologies. As such, generating spatio-temporal measurements that are not meter-based has become an important tool for modeling and forecasting. Over the past several decades, remote sensing technologies (instrumentation and analysis techniques) have been developed for this task using a variety of overhead and ground-based platforms to quantify the characteristics of energy consumption and end use on multiple spatiotemporal scales. In addition, tremendous progress in the fields of computer vision and machine learning has opened up significant opportunities for the analysis of large-scale remote sensing data. This Special Issue is focused on leveraging new and state-of-the-art remote sensing techniques for measuring and monitoring energy consumption at multiple spatial and temporal scales in both urban and rural environments.

Dr. Gregory Dobler
Guest Editor

Manuscript Submission Information

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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. Remote Sensing 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 2700 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

energy consumption
energy monitoring
satellite remote sensing
ground-based remote sensing
multi- and hyperspectral imaging
infrared and thermal imaging

Published Papers (7 papers)

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Research

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22 pages, 10973 KiB

Open AccessArticle

An Assessment of Electric Power Consumption Using Random Forest and Transferable Deep Model with Multi-Source Data

by Luxiao Cheng, Ruyi Feng, Lizhe Wang, Jining Yan and Dong Liang

Remote Sens. 2022, 14(6), 1469; https://doi.org/10.3390/rs14061469 - 18 Mar 2022

Cited by 1 | Viewed by 2427

Abstract

Reliable and fine-resolution electric power consumption (EPC) is essential for effective urban electricity allocation and planning. Currently, EPC data exists mainly as statistics with low resolution. Many studies estimate fine-resolution EPC based on the positive correction between stable nighttime light and EPC distribution. However, EPC is related to various factors other than nighttime light and is spatially non-stationary. Yet this has been ignored in current research. This study developed a novel method to estimate EPC at 500 m resolution by considering spatially non-stationary through fusing geospatial data and high-resolution satellite images. Deep transfer learning and statistical methods were used to extract socio-economic, population density, and landscape features to describe EPC distribution from multi-source geospatial data. Finally, a random forest regression (RFR) model with features and EPC statistics is established to estimate fine-resolution EPC. A study area of Shenzhen city, China, is employed to evaluate the proposed method. The

R^{2}

between predicted EPC and statistical EPC is

0.82

at sub-district level in 2013, which is higher than an existing EPC product (Shi’s product) with

R^{2} = 0.46

, illustrating the effectiveness of the proposed method. Moreover, the EPC distribution for Shenzhen from 2013 to 2019 was estimated. Furthermore, the spatiotemporal dynamic of EPC was analyzed at the pixel and sub-district levels. Full article

(This article belongs to the Special Issue Monitoring and Assessment of Energy Consumption through Remote Sensing)

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21 pages, 6314 KiB

Open AccessArticle

Estimating District-Level Electricity Consumption Using Remotely Sensed Data in Eastern Economic Corridor, Thailand

by Sirikul Hutasavi and Dongmei Chen

Remote Sens. 2021, 13(22), 4654; https://doi.org/10.3390/rs13224654 - 18 Nov 2021

Cited by 2 | Viewed by 2548

Abstract

The intensive industrial development in special economic zones, such as Thailand’s Eastern Economic Corridor, increases energy consumption, leading to an imbalance of energy supply and a challenge for energy management. Electricity consumption at a local level is crucial for utility planners to manage and invest in the electrical grid. With this study, we propose an electricity consumption estimation model at the district level using machine learning with publicly available statistical data and built-up area (BU), area of lit (AL), and sum of light intensity (SL) data extracted from Landsat 8 and Suomi NPP satellite nighttime light images. The models created from three machine learning algorithms, which included Multiple Linear Regression (MR), Decision Tree (DT), and Support Vector Regression (SVR), were compared. The results show that (1) electricity consumption is highly correlated with SL, AL, and BU; and (2) the DT model demonstrated a better performance in predicting local electricity consumption when compared to MR and SVR with the lowest error rate and highest R². The local government in developing countries with limited data and financial resources can adopt the proposed approach to benefit from utilizing commonly available remote sensing and statistical data with simple machine learning models such as DT (regression method) for sustainable electricity management. Full article

(This article belongs to the Special Issue Monitoring and Assessment of Energy Consumption through Remote Sensing)

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24 pages, 10856 KiB

Open AccessArticle

Surface Urban Heat Islands Dynamics in Response to LULC and Vegetation across South Asia (2000–2019)

by Talha Hassan, Jiahua Zhang, Foyez Ahmed Prodhan, Til Prasad Pangali Sharma and Barjeece Bashir

Remote Sens. 2021, 13(16), 3177; https://doi.org/10.3390/rs13163177 - 11 Aug 2021

Cited by 18 | Viewed by 4479

Abstract

Urbanization is an increasing phenomenon around the world, causing many adverse effects in urban areas. Urban heat island is are of the most well-known phenomena. In the present study, surface urban heat islands (SUHI) were studied for seven megacities of the South Asian countries from 2000–2019. The urban thermal environment and relationship between land surface temperature (LST), land use landcover (LULC) and vegetation were examined. The connection was explored with remote-sensing indices such as urban thermal field variance (UTFVI), surface urban heat island intensity (SUHII) and normal difference vegetation index (NDVI). LULC maps are classified using a CART machine learning classifier, and an accuracy table was generated. The LULC change matrix shows that the vegetated areas of all the cities decreased with an increase in the urban areas during the 20 years. The average LST in the rural areas is increasing compared to the urban core, and the difference is in the range of 1–2 (°C). The SUHII linear trend is increasing in Delhi, Karachi, Kathmandu, and Thimphu, while decreasing in Colombo, Dhaka, and Kabul from 2000–2019. UTFVI has shown the poor ecological conditions in all urban buffers due to high LST and urban infrastructures. In addition, a strong negative correlation between LST and NDVI can be seen in a range of −0.1 to −0.6. Full article

(This article belongs to the Special Issue Monitoring and Assessment of Energy Consumption through Remote Sensing)

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23 pages, 17738 KiB

Open AccessArticle

Estimation of the Potential Achievable Solar Energy of the Buildings Using Photogrammetric Mesh Models

by Yunsheng Zhang, Zhisheng Dai, Weixi Wang, Xiaoming Li, Siyang Chen and Li Chen

Remote Sens. 2021, 13(13), 2484; https://doi.org/10.3390/rs13132484 - 25 Jun 2021

Cited by 5 | Viewed by 2215

Abstract

Estimating the potential achievable solar energy in urban buildings is significantly important for the long-term planning and development of environmental protection strategies. Nevertheless, conventional methods based on LiDAR data are often costly and require more than one platform to obtain complete building surface information. The development of oblique photogrammetry technology enables fast and accurate acquiring of the 3D information of the surface. In this paper, we propose an efficient method to estimate the potential achievable solar energy of building surfaces based on photogrammetric mesh models. In this method, we use photogrammetric mesh models as the input data and then propose a hierarchical algorithm for shadow analysis. Combined with the solar radiation model, we then obtain the potential achievable solar energy of the building surface. We further investigate the performance of the proposed method and it is shown that this method outperforms the multi-source LiDAR data. Full article

(This article belongs to the Special Issue Monitoring and Assessment of Energy Consumption through Remote Sensing)

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23 pages, 48952 KiB

Open AccessArticle

Comparison of Accuracy of Surface Temperature Images from Unmanned Aerial Vehicle and Satellite for Precise Thermal Environment Monitoring of Urban Parks Using In Situ Data

by Dongwoo Kim, Jaejin Yu, Jeongho Yoon, Seongwoo Jeon and Seungwoo Son

Remote Sens. 2021, 13(10), 1977; https://doi.org/10.3390/rs13101977 - 19 May 2021

Cited by 15 | Viewed by 2902

Abstract

Rapid urbanization has led to several severe environmental problems, including so-called heat island effects, which can be mitigated by creating more urban green spaces. However, the temperature of various surfaces differs and precise measurement and analyses are required to determine the “coolest” of these. Therefore, we evaluated the accuracy of surface temperature data based on thermal infrared (TIR) cameras mounted on unmanned aerial vehicles (UAVs), which have recently been utilized for the spatial analysis of surface temperatures. Accordingly, we investigated land surface temperatures (LSTs) in green spaces, specifically those of different land cover types in an urban park in Korea. We compared and analyzed LST data generated by a thermal infrared (TIR) camera mounted on an unmanned aerial vehicle (UAV) and LST data from the Landsat 8 satellite for seven specific periods. For comparison and evaluation, we measured in situ LSTs using contact thermometers. The UAV TIR LST showed higher accuracy (R² 0.912, root mean square error (RMSE) 3.502 °C) than Landsat TIR LST accuracy (R² value lower than 0.3 and RMSE of 7.246 °C) in all periods. The Landsat TIR LST did not show distinct LST characteristics by period and land cover type; however, grassland, the largest land cover type in the study area, showed the highest accuracy. With regard to the accuracy of the UAV TIR LST by season, the accuracy was higher in summer and spring (

R^{2}

0.868–0.915, RMSE 2.523–3.499 °C) than in autumn and winter (

R^{2}

0.766–0.79, RMSE 3.834–5.398 °C). Some land cover types (concrete bike path, wooden deck) were overestimated, showing relatively high total RMSEs of 4.439 °C and 3.897 °C, respectively, whereas grassland, which has lower LST, was underestimated—showing a total RMSE of 3.316 °C. Our results showed that the UAV TIR LST could be measured with sufficient reliability for each season and land cover type in an urban park with complex land cover types. Accordingly, our results could contribute to decision-making for urban spaces and environmental planning in consideration of the thermal environment. Full article

(This article belongs to the Special Issue Monitoring and Assessment of Energy Consumption through Remote Sensing)

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18 pages, 3713 KiB

Open AccessArticle

Spatiotemporal Variations of City-Level Carbon Emissions in China during 2000–2017 Using Nighttime Light Data

by Yu Sun, Sheng Zheng, Yuzhe Wu, Uwe Schlink and Ramesh P. Singh

Remote Sens. 2020, 12(18), 2916; https://doi.org/10.3390/rs12182916 - 08 Sep 2020

Cited by 26 | Viewed by 4074

Abstract

China is one of the largest carbon emitting countries in the world. Numerous strategies have been considered by the Chinese government to mitigate carbon emissions in recent years. Accurate and timely estimation of spatiotemporal variations of city-level carbon emissions is of vital importance for planning of low-carbon strategies. For an assessment of the spatiotemporal variations of city-level carbon emissions in China during the periods 2000–2017, we used nighttime light data as a proxy from two sources: Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) data and the Suomi National Polar-orbiting Partnership satellite’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). The results show that cities with low carbon emissions are located in the western and central parts of China. In contrast, cities with high carbon emissions are mainly located in the Beijing-Tianjin-Hebei region (BTH) and Yangtze River Delta (YRD). Half of the cities of China have been making efforts to reduce carbon emissions since 2012, and regional disparities among cities are steadily decreasing. Two clusters of high-emission cities located in the BTH and YRD followed two different paths of carbon emissions owing to the diverse political status and pillar industries. We conclude that carbon emissions in China have undergone a transformation to decline, but a very slow balancing between the spatial pattern of high-emission versus low-emission regions in China can be presumed. Full article

(This article belongs to the Special Issue Monitoring and Assessment of Energy Consumption through Remote Sensing)

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14 pages, 8446 KiB

Open AccessTechnical Note

Monitoring and Evaluating Nature-Based Solutions Implementation in Urban Areas by Means of Earth Observation

by Nektarios Chrysoulakis, Giorgos Somarakis, Stavros Stagakis, Zina Mitraka, Man-Sing Wong and Hung-Chak Ho

Remote Sens. 2021, 13(8), 1503; https://doi.org/10.3390/rs13081503 - 14 Apr 2021

Cited by 9 | Viewed by 4990

Abstract

Climate change influences the vulnerability of urban populations worldwide. To improve their adaptive capacity, the implementation of nature-based solutions (NBS) in urban areas has been identified as an appropriate action, giving urban planning and development an important role towards climate change adaptation/mitigation and risk management and resilience. However, the importance of extensively applying NBS is still underestimated, especially regarding its potential to induce significantly positive environmental and socioeconomic impacts across cities. Concerning environmental impacts, monitoring and evaluation is an important step of NBS management, where earth observation (EO) can contribute. EO is known for providing valuable disaggregated data to assess the modifications caused by NBS implementation in terms of land cover, whereas the potential of EO to uncover the role of NBS in urban metabolism modifications (e.g., energy, water, and carbon fluxes and balances) still remains underexplored. This study reviews the EO potential in the monitoring and evaluation of NBS implementation in cities, indicating that satellite observations combined with data from complementary sources may provide an evidence-based approach in terms of NBS adaptive management. EO-based tools can be applied to assess NBS’ impacts on urban energy, water, and carbon balances, further improving our understanding of urban systems dynamics and supporting sustainable urbanization. Full article

(This article belongs to the Special Issue Monitoring and Assessment of Energy Consumption through Remote Sensing)

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