Monitoring spatial sustainable development: Semi-automated analysis of satellite and aerial images for energy transition and sustainability indicators

Solar panels are installed by a large and growing number of households due to the convenience of having cheap and renewable energy to power house appliances. In contrast to other energy sources solar installations are distributed very decentralized and spread over hundred-thousands of locations. On a global level more than 25% of solar photovoltaic (PV) installations were decentralized. The effect of the quick energy transition from a carbon based economy to a green economy is though still very difficult to quantify. As a matter of fact the quick adoption of solar panels by households is difficult to track, with local registries that miss a large number of the newly built solar panels. This makes the task of assessing the impact of renewable energies an impossible task. Although models of the output of a region exist, they are often black box estimations. This project's aim is twofold: First automate the process to extract the location of solar panels from aerial or satellite images and second, produce a map of solar panels along with statistics on the number of solar panels. Further, this project takes place in a wider framework which investigates how official statistics can benefit from new digital data sources. At project completion, a method for detecting solar panels from aerial images via machine learning will be developed and the methodology initially developed for BE, DE and NL will be standardized for application to other EU countries. In practice, machine learning techniques are used to identify solar panels in satellite and aerial images for the province of Limburg (NL), Flanders (BE) and North Rhine-Westphalia (DE).Comment: This document provides the reader with an overview of the various datasets which will be used throughout the project. The collection of satellite and aerial images as well as auxiliary information such as the location of buildings and roofs which is required to train, test and validate the machine learning algorithm that is being develope

Solar Energy Forecast for Integration of Grid and Balancing Power Using Profound Learning

The rapid and unexpected advancements in solar photovoltaic (PV) technology pose a future challenge for power sector experts responsible for managing the distribution of electricity, given the technology’s direct reliance on atmospheric and weather conditions. Therefore, the development of reliable predictive models for short-term solar PV generation forecasts becomes critically important to maintain a stable power supply and ensure seamless grid operations. With the evolution of deep learning and its intricate models, its application in this domain offers a more efficient means of achieving precise forecasts. As a result, the proposed system undergoes the following stages: a) Collecting data from the Sky Images and Photovoltaic Power Generation Dataset (SKIPDD) hosted on a GitHub repository, which contains one-minute intervals of 64x64 sky images and concurrent PV power generation data. b) Enhancing the PV input data through processes such as geometric correction, ortho rectification, pan sharpening, block adjustment, and histogram equalization. c) Extracting PV-related features from these images using an Autoencoder. d) forecasting using integration of CNNbased Bi-LSTM. Experimental evaluation states that the proposed system (ACNN-BiLSTM) outperforms better on various measures (accuracy:0.95, MSE:0.08, MAE: 0.02)

Assessing placement efficiency of photovoltaic installations using Mask R-CNN

Photovoltaic (PV) energy production has experienced strong growth over the past years and is forecasted to greatly contribute to the successful transition to renewable energy production as demanded by Switzerland’s Energy Strategy 2050. Several studies attempted to estimate the national PV potential on building rooftops but arrived at strongly varying results ranging from 15 to 53 TWh annually. To a vast extent, the differences can be explained by the application of varying rooftop utilization ratios which were extrapolated by all previous studies. Moreover, no comparison of the placement of existing PV installations to the suitability categorization from the sonnendach.ch project was yet carried out. Therefore, the aim of this master thesis was to develop and evaluate a prototype methodology to close the research gaps regarding rooftop utilization ratio and the efficiency of PV panel placement. The prototype methodology to answer these questions was developed in Python and leverages publicly available data from the Swiss government in conjunction with a Mask R-CNN for the accurate segmentation of PV panels on high resolution aerial imagery. A total of 1130 individual images of building rooftop were thereby collected in the canton of Aargau of which 974 were used to train the Mask R-CNN model. After four training iterations with varying dataset sizes, the segmentation performance of the Mask R-CNN achieved an iou_score of 0.74. Overall, the rooftop utilization ratio found in this thesis equated to 29%, suggesting that all PV potential studies systematically overestimate the extent of rooftop utilization. Moreover, the findings of this thesis suggest that the more suitable a rooftop area is, the greater its extent of utilization whereas previous studies assumed a uniform distribution of utilization ratio across all suitability categorizations. From the assessed building rooftops, 2.8% have their PV panels suboptimally placed and therefore fail to efficiently exploit solar radiation. 71% of which were successfully detected by the model. Overall, the findings of this thesis proved that an automated, large-scale assessment of PV placement efficiency is technically feasible. This information could support national energy planning as well as PV incentive decision making. However, the segmentation performance of the Mask R-CNN achieved with the resources available to this thesis is currently insufficient for detailed quantitative analyses. Consequently, further studies to improve the Mask R-CNN performance should be conducted before applying the prototype methodology on a large scale

Assessment of potential rooftop solar PV electricity at a suburban scale, and a comparative analysis based on topographical obstruction and seasonality

Long-term climate change mitigation calls for a switch from the current global non-renewable energy system to low greenhouse gas (GHG) emission energy solutions. Many nations have started adopting energy-efficient technology as part of their climate change programs and the built environment has been identified as a key lever for reducing emissions linked to energy efficiency. Building rooftop photovoltaic (PV) system is an effective technology to reduce emissions through the use of solar energy. In recent years, rooftop PV systems have become the main source of solar-generated energy, and forecasting their output is critical when assessing a site\u27s PV energy potential. However, integrating topographical features with seasonal considerations to estimate solar PV energy is challenging. There are some studies available that estimate solar PV energy on rooftops using geospatial tool modeling, but these have limitations in functionality, accuracy, and calculation speed. This study uses a geospatial tool to assess the solar PV potential of suitable rooftops in the suburbs of Wollongong, Australia, namely, Wombarra and Cringila. The model used in this study compares the energy potential of these two suburbs based on the topographical feature (escarpment), seasonality, rooftop slope, and aspect. The digital surface model (DSM) is created using LiDAR data, and then the DSM, building footprints, and suburb boundaries data are used to calculate the solar PV energy potential. A total of 1594 buildings from two suburbs were considered. Subsequently, solar radiation modeling for four common seasons in a year and a comparison of solar radiation output, suitable rooftop area, and electricity output are being done for both suburbs. Wombarra\u27s building rooftops are shadowed by the escarpment, whereas Cringila\u27s aren\u27t. Even though the weather in both suburbs is similar, the escarpment\u27s shadow affects solar PV energy output. Wombarra has 178 kWh/m2/building lesser yearly solar radiation than Cringila. Hence, Cringila offers more solar rooftop installation potential per building. The average annual potential electricity generation per dwelling in Wombarra is 20.6 kWh/m2/day, and the same for Cringila is 27.6 kWh/m2/day. The outcome reveals that 1352 building rooftops, with a usable area of 75481 m2, are the best locations for installing solar panels. According to the Australian Government\u27s Energy Made Easy statistics, the annual electricity consumption per household in Wollongong is 5707.6 kWh (Australian Energy Regulator 2022). The estimated yearly electricity production is 12705 Mwh (Wombarra: 2778.3 Mwh, Cringila: 9926.7 Mwh), which would be sufficient to meet local electricity consumption. An excess of 17% from Wombarra and 48% from Cringila can be exported back to the grid, which can be used by 3 neighbouring areas. Tiseo (2021) reported that Australia\u27s power sector released 656.4 grams/kWh of CO2 in 2020. Therefore, solar PV panels on all suitable rooftops of both suburbs could prevent 8339.5 tonnes of CO2 emissions. To achieve the goal of clean energy, future development can use the study\u27s findings as a guide. The proposed approach can assist in influencing policies and subsidies to boost deployment. This research can be made more in-depth by taking into account social and economic factors like consumer choices and return on investment, and physically inspecting specific building rooftop impediments

Solar irradiance forecast from all-sky images using machine learning

The novel method presented here comprises techniques for cloud coverage percentage forecasts, cloud movement forecast and the subsequently prediction of the global horizontal irradiance (GHI) using all-sky images and Machine Learning techniques. Such models are employed to forecast GHI, which is necessary to make more accurate time series forecasts for photovoltaic systems like “island solutions” for power production or for energy exchange like in virtual power plants. All images were recorded by a hemispheric sky imager (HSI) at the Institute of Meteo rology and Climatology (IMuK) of the Leibniz University Hannover, Hannover, Germany. This thesis is composed of three parts. First, a model to forecast the total cloud cover five-minutes ahead by training an autoregressive neural network with Backpropagation. The prediction results showed a reduction of both the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) by approximately 30% compared to the reference solar persistence solar model for various cloud conditions. Second, a model to predict the GHI up to one-hour ahead by training a Levenberg Marquardt Backpropagation neural network. This novel method reduced both the RMSE and the MAE of the one-hour prediction by approximately 40% under various weather conditions. Third, for the forecasting of the cloud movement up to two-minutes ahead, a high-resolution Deep Learning method using convolutional neural networks (CNN) was created. By taking real cloud shapes produced by the correction of the hazy areas considering the green signal counts pixels, predicted clouds shapes of the proposed algorithm was compared with the persistence solar model using the Sørensen-Dice similarity coefficient (SDC). The results of the proposed method have shown a mean SDC of 94 ± 2.6% (mean ± standard deviation) for the first minutes outperforming the persistence solar model with a SDC of 89 ± 3.8%. Thus, the proposed method may represent cloud shapes better than the persistence solar model. Finally, the Bonferroni's correction was performed so that the significance level of 0.05 was corrected to 0.05, and thus, the difference between the SDC of the proposed method and the persistence solar model was p = 0.001 being significantly high. The proposed methodologies may have broad application in the planning and management of PV power production allowing more accurate forecasts of the GHI minutes ahead by targeting primary and secondary energy control reserve

Data driven tools to assess the location of photovoltaic facilities in urban areas

Urban sustainability is a significant factor in combating climate change. Replacing polluting by renewable energies is fundamental to reduce the emission of greenhouse gases. Photovoltaic (PV) facilities harnessing solar energy, and particularly self-consumption PV facilities, can be widely used in cities throughout most countries. Therefore, locating spaces where photovoltaic installations can be integrated into urban areas is essential to reduce climate change and improve urban sustainability. An open-source software (URSUS-PV) to aid decision-making regarding possible optimal locations for photovoltaic panel installations in cities is presented in this paper. URSUS-PV is the result of a data mining process, and it can extract the characteristics of the roofs (orientation, inclination, latitude, longitude, area) in the urban areas of interest. By combining this information with meteorological data and characteristics of the photovoltaic systems, the system can predict both the next-day hourly photovoltaic energy production and the long-term photovoltaic daily average energy production.This work has been supported by the project RTI2018-095097-B-I00 at the 2018 call for I+D+i Project of the Ministerio de Ciencia, Innovación y Universidades, Spain. Funding for open access charge: Universidad de Málaga/CBUA, Spain

Advanced Operation and Maintenance in Solar Plants, Wind Farms and Microgrids

This reprint presents advances in operation and maintenance in solar plants, wind farms and microgrids. This compendium of scientific articles will help clarify the current advances in this subject, so it is expected that it will please the reader