scholarly journals A Hybrid Data-Driven Online Solar Energy Disaggregation System from the Grid Supply Point

2021 ◽  
Author(s):  
Xiao-Yu Zhang ◽  
Chris Watkins ◽  
Stefanie Kuenzel
Keyword(s):  
Solar Energy ◽  
Short Term Memory ◽  
Meteorological Data ◽  
Ground Truth ◽  
Small Scale ◽  
Online Information ◽  
Pv Systems ◽  
Wide Range ◽  
Energy Disaggregation ◽  
Disaggregation System

The integration of small-scale PV systems (such as roof-top PVs) decreases the visibility of the power system since the real demand load is masked. Most of the rooftop systems are behind-the-meter and cannot be measured by the household smart meter. To overcome the challenges mentioned above, this paper proposes an online solar energy disaggregation system to decouple the solar energy generated by the roof-top PV systems and ground truth demand load from the net measurements. A 1D CNN bidirectional long short-term memory (CNN-BiLSTM) deep learning method is used as the core algorithm of the proposed system. The system takes a wide range of online information (AMI data, meteorological data, satellite-driven irradiance, and temporal information) as inputs to evaluate the PV generation, and the system also enables online and offline modes. The effectiveness of the proposed algorithm is evaluated by comparing it to baselines. The results show that the proposed method reaches good performance under different penetration rates and different feeder levels. Finally, a transfer learning process is introduced to verify the proposed system has good robustness and can be applied to anywhere else easily.

scholarly journals A Hybrid Data-Driven Online Solar Energy Disaggregation System from the Grid Supply Point

2021 ◽  
Author(s):  
Xiao-Yu Zhang ◽  
Chris Watkins ◽  
Stefanie Kuenzel
Keyword(s):  
Solar Energy ◽  
Short Term Memory ◽  
Meteorological Data ◽  
Ground Truth ◽  
Small Scale ◽  
Online Information ◽  
Pv Systems ◽  
Wide Range ◽  
Energy Disaggregation ◽  
Disaggregation System

The integration of small-scale PV systems (such as roof-top PVs) decreases the visibility of the power system since the real demand load is masked. Most of the rooftop systems are behind-the-meter and cannot be measured by the household smart meter. To overcome the challenges mentioned above, this paper proposes an online solar energy disaggregation system to decouple the solar energy generated by the roof-top PV systems and ground truth demand load from the net measurements. A 1D CNN bidirectional long short-term memory (CNN-BiLSTM) deep learning method is used as the core algorithm of the proposed system. The system takes a wide range of online information (AMI data, meteorological data, satellite-driven irradiance, and temporal information) as inputs to evaluate the PV generation, and the system also enables online and offline modes. The effectiveness of the proposed algorithm is evaluated by comparing it to baselines. The results show that the proposed method reaches good performance under different penetration rates and different feeder levels. Finally, a transfer learning process is introduced to verify the proposed system has good robustness and can be applied to anywhere else easily.

scholarly journals Remote sensing of ecosystem light use efficiency with MODIS-based PRI

2011 ◽  
Vol 8 (1) ◽  
pp. 189-202 ◽  
Author(s):  
A. Goerner ◽  
M. Reichstein ◽  
E. Tomelleri ◽  
N. Hanan ◽  
S. Rambal ◽  
...  
Keyword(s):  
Meteorological Data ◽  
Vapour Pressure Deficit ◽  
Ground Truth ◽  
Light Use Efficiency ◽  
Wide Range ◽  
Evaluation Exercise ◽  
Use Efficiency ◽  
Study Sites ◽  
Absorbed Photosynthetically Active Radiation

Abstract. Several studies sustained the possibility that a photochemical reflectance index (PRI) directly obtained from satellite data can be used as a proxy for ecosystem light use efficiency (LUE) in diagnostic models of gross primary productivity. This modelling approach would avoid the complications that are involved in using meteorological data as constraints for a fixed maximum LUE. However, no unifying model predicting LUE across climate zones and time based on MODIS PRI has been published to date. In this study, we evaluate the effectiveness with which MODIS-based PRI can be used to estimate ecosystem light use efficiency at study sites of different plant functional types and vegetation densities. Our objective is to examine if known limitations such as dependence on viewing and illumination geometry can be overcome and a single PRI-based model of LUE (i.e. based on the same reference band) can be applied under a wide range of conditions. Furthermore, we were interested in the effect of using different faPAR (fraction of absorbed photosynthetically active radiation) products on the in-situ LUE used as ground truth and thus on the whole evaluation exercise. We found that estimating LUE at site-level based on PRI reduces uncertainty compared to the approaches relying on a maximum LUE reduced by minimum temperature and vapour pressure deficit. Despite the advantages of using PRI to estimate LUE at site-level, we could not establish an universally applicable light use efficiency model based on MODIS PRI. Models that were optimised for a pool of data from several sites did not perform well.

Investigation on Solar-Powered Turbines Using Refrigerant R-134A

2014 ◽  
Author(s):  
Jihad Rishmany ◽  
Michel Daaboul ◽  
Issam Tawk ◽  
Nicolas Saba
Keyword(s):  
Power Generation ◽  
Solar Energy ◽  
Fossil Fuels ◽  
Rankine Cycle ◽  
Working Fluid ◽  
Small Scale ◽  
Combustion Gases ◽  
Water Heaters ◽  
Wide Range ◽  
R 134A

Renewable energy has become a promising solution to substitute fossil fuels in power generation. In particular, the use of solar energy is stretched to a wide range of applications, e.g. photovoltaic cells, solar water heaters, solar space heating, solar thermal plants. However, the combination of solar energy with the Rankine cycle is limited to few applications only. In this context, this study aims in investigating the practicality of employing solar heaters to operate a Rankine cycle for small scale power generation. The working fluid in this study is refrigerant R-134a. Sizing and calculations of the various components of the system are carried out based on a net output power of 1 kW. In comparison with available electricity sources in Lebanon, it was found that the proposed system is currently more expensive than public electricity. However, it can compete with private generators that currently fill the gap in electricity shortage. The main advantage herein lies in the friendly environmental load due to the absence of combustion gases.

scholarly journals Efficiency of maximum power point tracking in photovoltaic system under variable solar irradiance

2014 ◽  
Vol 62 (4) ◽  
pp. 713-721 ◽  
Author(s):  
W. Marańda ◽  
M. Piotrowicz
Keyword(s):  
Solar Energy ◽  
Solar Irradiance ◽  
Tracking Algorithm ◽  
Maximum Power ◽  
Field Conditions ◽  
Energy Output ◽  
Maximum Power Point ◽  
Pv Systems ◽  
Wide Range ◽  
Power Point

Abstract Field conditions decrease the energy output of photovoltaic (PV) systems, mainly due to excessive temperatures. However, in regions with moderate ambient temperatures, as in Poland, solar energy is commonly delivered with highly fluctuating irradiance. This introduces yet another source of energy losses due to the non-ideal tracking of actual position of Maximum Power Point (MPP). Majority of PV-systems are equipped with DC/AC and grid-connected inverter. Since the solar energy flux is variable, an adequate MPP-tracking algorithm is required to handle a wide range of load levels and face rapid changes of input power. Along with the essential DC/AC conversion, the quality of MPP-tracking must also be taken into account in evaluation of inverter efficiency. The tracking in dynamic conditions has been addressed only recently. Several algorithms has been studied theoretically, experimentally or in laboratory conditions by applying artificial input test-patterns. This work takes the opposite approach by applying the recorded real-life solar irradiance and simulating the tracking behavior to study the problem for true field conditions in Poland. The simulation uses the unique high-quality irradiance data collected with 200 ms time resolution. The calculation of both static and dynamic MPP-tracking efficiency has been performed for representative variable-cloudy day, applying commonly used Perturb&Observe tracking algorithm.

scholarly journals Remote sensing of ecosystem light use efficiency with MODIS-based PRI – the DOs and DON'Ts

2010 ◽  
Vol 7 (5) ◽  
pp. 6935-6969 ◽  
Author(s):  
A. Goerner ◽  
M. Reichstein ◽  
E. Tomelleri ◽  
N. Hanan ◽  
S. Rambal ◽  
...  
Keyword(s):  
Meteorological Data ◽  
Vapour Pressure Deficit ◽  
Ground Truth ◽  
Light Use Efficiency ◽  
Wide Range ◽  
Evaluation Exercise ◽  
Use Efficiency ◽  
Study Sites ◽  
Absorbed Photosynthetically Active Radiation

Abstract. Several studies sustained the possibility that a photochemical reflectance index (PRI) directly obtained from satellite data can be used as a proxy for ecosystem light use efficiency (LUE) in diagnostic models of gross primary productivity. This modelling approach would avoid the complications that are involved in using meteorological data as constraints for a fixed maximum LUE. However, no unifying model predicting LUE across climate zones and time based on MODIS PRI has been published to date. In this study, we evaluate the efficiency with which MODIS-based PRI can be used to estimate ecosystem light use efficiency at study sites of different plant functional types and vegetation densities. Our objective is to examine if known limitations such as dependance on viewing and illumination geometry can be overcome and a single PRI-based model of LUE (i.e. based on the same reference band) can be applied under a wide range of conditions. Furthermore, we were interested in the effect of using different faPAR (fraction of absorbed photosynthetically active radiation) products on the in-situ LUE used as ground truth and thus on the whole evaluation exercise. We found that estimating LUE at site-level based on PRI reduces uncertainty compared to the approaches relying on a maximum LUE reduced by minimum temperature and vapour pressure deficit. Despite the advantages of using PRI to estimate LUE at site-level, we could not establish an universally applicable light use efficiency model based on MODIS PRI. Models that were optimised for a pool of data from several sites did not perform well.

Energy balance and performance assessment of PV systems installed at a positive-energy building (PEB) solar energy research centre

Solar Energy ◽  
2020 ◽  
Vol 212 ◽  
pp. 258-274
Author(s):  
C. Zomer ◽  
I. Custódio ◽  
S. Goulart ◽  
S. Mantelli ◽  
G. Martins ◽  
...  
Keyword(s):  
Energy Balance ◽  
Solar Energy ◽  
Performance Assessment ◽  
Research Centre ◽  
Positive Energy ◽  
Energy Research ◽  
Pv Systems ◽  
And Performance

Integrated Design and Multi-Objective Optimization of a Single Stage Heat-Pump Turbocompressor

2013 ◽  
Author(s):  
J. Schiffmann
Keyword(s):  
Design Optimization ◽  
High Efficiency ◽  
Integrated Design ◽  
Heat Pumps ◽  
Small Scale ◽  
Multi Objective Optimization ◽  
Design Constraints ◽  
Multi Objective ◽  
Wide Range ◽  
Standard Design

Small scale turbomachines in domestic heat pumps reach high efficiency and provide oil-free solutions which improve heat-exchanger performance and offer major advantages in the design of advanced thermodynamic cycles. An appropriate turbocompressor for domestic air based heat pumps requires the ability to operate on a wide range of inlet pressure, pressure ratios and mass flows, confronting the designer with the necessity to compromise between range and efficiency. Further the design of small-scale direct driven turbomachines is a complex and interdisciplinary task. Textbook design procedures propose to split such systems into subcomponents and to design and optimize each element individually. This common procedure, however, tends to neglect the interactions between the different components leading to suboptimal solutions. The authors propose an approach based on the integrated philosophy for designing and optimizing gas bearing supported, direct driven turbocompressors for applications with challenging requirements with regards to operation range and efficiency. Using previously validated reduced order models for the different components an integrated model of the compressor is implemented and the optimum system found via multi-objective optimization. It is shown that compared to standard design procedure the integrated approach yields an increase of the seasonal compressor efficiency of more than 12 points. Further a design optimization based sensitivity analysis allows to investigate the influence of design constraints determined prior to optimization such as impeller surface roughness, rotor material and impeller force. A relaxation of these constrains yields additional room for improvement. Reduced impeller force improves efficiency due to a smaller thrust bearing mainly, whereas a lighter rotor material improves rotordynamic performance. A hydraulically smoother impeller surface improves the overall efficiency considerably by reducing aerodynamic losses. A combination of the relaxation of the 3 design constraints yields an additional improvement of 6 points compared to the original optimization process. The integrated design and optimization procedure implemented in the case of a complex design problem thus clearly shows its advantages compared to traditional design methods by allowing a truly exhaustive search for optimum solutions throughout the complete design space. It can be used for both design optimization and for design analysis.

scholarly journals Global soil moisture data derived through machine learning trained with in-situ measurements

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Large Scale ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Soil Moisture Data ◽  
Wide Range ◽  
Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

scholarly journals Using Satellite Data for CBRN (Chemical, Biological, Radiological, and Nuclear) Threat Detection, Monitoring, and Modelling

2021 ◽  
Author(s):  
Gary Sutlieff ◽  
Lucy Berthoud ◽  
Mark Stinchcombe
Keyword(s):  
Satellite Data ◽  
Meteorological Data ◽  
Atmospheric Composition ◽  
Chemical Detection ◽  
List Type ◽  
Data Types ◽  
Wide Range ◽  
Future Technologies ◽  
The Impact ◽  
Near Future

Abstract CBRN (Chemical, Biological, Radiological, and Nuclear) threats are becoming more prevalent, as more entities gain access to modern weapons and industrial technologies and chemicals. This has produced a need for improvements to modelling, detection, and monitoring of these events. While there are currently no dedicated satellites for CBRN purposes, there are a wide range of possibilities for satellite data to contribute to this field, from atmospheric composition and chemical detection to cloud cover, land mapping, and surface property measurements. This study looks at currently available satellite data, including meteorological data such as wind and cloud profiles, surface properties like temperature and humidity, chemical detection, and sounding. Results of this survey revealed several gaps in the available data, particularly concerning biological and radiological detection. The results also suggest that publicly available satellite data largely does not meet the requirements of spatial resolution, coverage, and latency that CBRN detection requires, outside of providing terrain use and building height data for constructing models. Lastly, the study evaluates upcoming instruments, platforms, and satellite technologies to gauge the impact these developments will have in the near future. Improvements in spatial and temporal resolution as well as latency are already becoming possible, and new instruments will fill in the gaps in detection by imaging a wider range of chemicals and other agents and by collecting new data types. This study shows that with developments coming within the next decade, satellites should begin to provide valuable augmentations to CBRN event detection and monitoring. Article Highlights There is a wide range of existing satellite data in fields that are of interest to CBRN detection and monitoring. The data is mostly of insufficient quality (resolution or latency) for the demanding requirements of CBRN modelling for incident control. Future technologies and platforms will improve resolution and latency, making satellite data more viable in the CBRN management field

scholarly journals A broadbanded pressure differential wave energy converter based on dielectric elastomer generators

2021 ◽  
Author(s):  
Michele Righi ◽  
Giacomo Moretti ◽  
David Forehand ◽  
Lorenzo Agostini ◽  
Rocco Vertechy ◽  
...  
Keyword(s):  
Wave Energy ◽  
Large Deformations ◽  
Dielectric Elastomer ◽  
Wave Energy Converter ◽  
Pressure Differential ◽  
Design Stage ◽  
Small Scale ◽  
Energy Converter ◽  
Wide Range ◽  
The Waves

AbstractDielectric elastomer generators (DEGs) are a promising option for the implementation of affordable and reliable sea wave energy converters (WECs), as they show considerable promise in replacing expensive and inefficient power take-off systems with cheap direct-drive generators. This paper introduces a concept of a pressure differential wave energy converter, equipped with a DEG power take-off operating in direct contact with sea water. The device consists of a closed submerged air chamber, with a fluid-directing duct and a deformable DEG power take-off mounted on its top surface. The DEG is cyclically deformed by wave-induced pressure, thus acting both as the power take-off and as a deformable interface with the waves. This layout allows the partial balancing of the stiffness due to the DEG’s elasticity with the negative hydrostatic stiffness contribution associated with the displacement of the water column on top of the DEG. This feature makes it possible to design devices in which the DEG exhibits large deformations over a wide range of excitation frequencies, potentially achieving large power capture in a wide range of sea states. We propose a modelling approach for the system that relies on potential-flow theory and electroelasticity theory. This model makes it possible to predict the system dynamic response in different operational conditions and it is computationally efficient to perform iterative and repeated simulations, which are required at the design stage of a new WEC. We performed tests on a small-scale prototype in a wave tank with the aim of investigating the fluid–structure interaction between the DEG membrane and the waves in dynamical conditions and validating the numerical model. The experimental results proved that the device exhibits large deformations of the DEG power take-off over a broad range of monochromatic and panchromatic sea states. The proposed model demonstrates good agreement with the experimental data, hence proving its suitability and effectiveness as a design and prediction tool.

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