scholarly journals Data-Driven Mitigation of Energy Scheduling Inaccuracy in Renewable-Penetrated Grids: Summerside Electric Use Case

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2228
Author(s):  
Mostafa Farrokhabadi
Keyword(s):  
Time Series ◽  
Energy Storage ◽  
Energy Cost ◽  
Prince Edward Island ◽  
State Of The Art ◽  
Negative Impact ◽  
Probabilistic Method ◽  
Data Driven ◽  
Renewable Energy Resources ◽  
The Impact

This paper presents findings on mitigating the negative impact of renewable energy resources variability on the energy scheduling problem, in particular for island grids and microgrids. The methods and findings presented in this paper are twofold. First, data obtained from the City of Summerside in the province of Prince Edward Island, Canada, is leveraged to demonstrate the effectiveness of state-of-the-art time series predictors in mitigating energy scheduling inaccuracy. Second, the outcome of the time series prediction analysis is used to propose a novel data-driven battery energy storage system (BESS) sizing study for energy scheduling purposes. The proposed probabilistic method accounts for intra-interval variations of generation and demand, thus mitigating the trade-off between time resolution of the problem formulation and the solution accuracy. In addition, as part of the sizing study, a BESS management strategy is proposed to minimize energy scheduling inaccuracies, and is then used to obtain the optimal BESS size. Finally, the paper presents quantitative analyses of the impact of both the energy predictors and the BESS on the supplied energy cost using the actual data of the Summerside Electric grid. The paper reveals the significant potential for reducing energy cost in renewable-penetrated grids and microgrids through state-of-the-art predictors combined with applications of properly-sized energy storage systems.

The Impact of the Change in Payment System for German Psychiatric Clinics on Treatment Success: An Interrupted Time Series Analysis

2019 ◽  
Vol 82 (06) ◽  
pp. 559-567
Author(s):  
Christina Niedermeier ◽  
Andrea Barrera ◽  
Eva Esteban ◽  
Ivana Ivandic ◽  
Carla Sabariego
Keyword(s):  
Time Series ◽  
Negative Impact ◽  
Treatment Success ◽  
Interrupted Time Series ◽  
Sensitivity Analyses ◽  
Interrupted Time Series Analysis ◽  
Post Intervention ◽  
Intervention Point ◽  
The Impact ◽  
New System

Abstract Background In Germany a new reimbursement system for psychiatric clinics was proposed in 2009 based on the § 17d KHG Psych-Entgeltsystem. The system can be voluntary implemented by clinics since 2013 but therapists are frequently afraid it might affect treatment negatively. Objectives To evaluate whether the new system has a negative impact on treatment success by analysing routinely collected data in a Bavarian clinic. Material and methods Aggregated data of 1760 patients treated in the years 2007–2016 was analysed with segmented regression analysis of interrupted time series to assess the effects of the system on treatment success, operationalized with three outcome variables. A negative change in level after a lag period was hypothesized. The robustness of results was tested by sensitivity analyses. Results The percentage of patients with treatment success tends to increase after the new system but no significant change in level was observed. The sensitivity analyses corroborate results for 2 outcomes but when the intervention point was shifted, the positive change in level for the third outcome became significant. Conclusions Our initial hypothesis is not supported. However, the sensitivity analyses disclosed uncertainties and our study has limitations, such as a short observation time post intervention. Results are not generalizable as data of a single clinic was analysed. Nevertheless, we show the importance of collecting and analysing routine data to assess the impact of policy changes on patient outcomes.

scholarly journals Data-driven, PCFG-based and Pseudo-PCFG-based Models for Chinese Dependency Parsing

2013 ◽  
Vol 1 ◽  
pp. 301-314 ◽  
Author(s):  
Weiwei Sun ◽  
Xiaojun Wan
Keyword(s):  
Comparative Study ◽  
State Of The Art ◽  
Data Driven ◽  
Dependency Parsing ◽  
Transition Graph ◽  
Final Model ◽  
System Combination ◽  
Pos Tagging ◽  
Heterogeneous Models ◽  
The Impact

We present a comparative study of transition-, graph- and PCFG-based models aimed at illuminating more precisely the likely contribution of CFGs in improving Chinese dependency parsing accuracy, especially by combining heterogeneous models. Inspired by the impact of a constituency grammar on dependency parsing, we propose several strategies to acquire pseudo CFGs only from dependency annotations. Compared to linguistic grammars learned from rich phrase-structure treebanks, well designed pseudo grammars achieve similar parsing accuracy and have equivalent contributions to parser ensemble. Moreover, pseudo grammars increase the diversity of base models; therefore, together with all other models, further improve system combination. Based on automatic POS tagging, our final model achieves a UAS of 87.23%, resulting in a significant improvement of the state of the art.

scholarly journals Time-Series PV Hosting Capacity Assessment with Storage Deployment

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2524 ◽  
Author(s):  
Magdalena Bartecka ◽  
Grazia Barchi ◽  
Józef Paska
Keyword(s):  
Time Series ◽  
Energy Storage ◽  
Low Voltage ◽  
Gaussian Mixture ◽  
Variable Load ◽  
Pv Systems ◽  
Voltage Deviation ◽  
Time Series Analyses ◽  
Capacity Selection ◽  
The Impact

Europe aims to diversify energy sources and reduce greenhouse gas emissions. On this field, large PV power growth is observed that may cause problems in existing networks. This paper examines the impact of distributed PV systems on voltage quality in a low voltage feeder in terms of the European standard EN 50160. As the standard defines allowable percentage of violation during one week period, time-series analyses are done to assess PV hosting capacity. The simulations are conducted with 10-minute step and comprise variable load profiles based on Gaussian Mixture Model and PV profiles based on a distribution with experimentally obtained parameters. In addition, the outcomes are compared with “snapshot” simulations. Next, it is examined how energy storage utilization affects the hosting capacity. Several deployments of energy storages are presented with different number and capacity. In particular, a greedy algorithm is proposed to determine the sub-optimal energy storage deployment based on the voltage deviation minimization. The simulations show that time-series analyses in comparison with snapshot analyses give completely different results and change the level of PV hosting capacity. Moreover, incorrect energy storage capacity selection and location may cause even deterioration of power quality in electrical systems with high RES penetration.

scholarly journals Does Foreign Direct Investment More Important Than Domestic Direct Investment for Export Structure? Evidence from Indonesia

2021 ◽  
Author(s):  
Ariyo DP Irhamna ◽  
Ely Nurhayati ◽  
Adinda Putri Safira ◽  
Galuh Indra Wijaya
Keyword(s):  
Time Series ◽  
Foreign Investment ◽  
Direct Investment ◽  
Negative Impact ◽  
Positive Impact ◽  
Domestic Investment ◽  
Fdi Inflows ◽  
Export Structure ◽  
The Impact ◽  
Export Product

Abstract Scholars have long studied the spillover of FDI on trade. However, there has been limited study which spesifically investigate the impact of FDI on the export structure in a developing country. Does FDI more important than domestic investment for export structure? To examine the question, we test the impact of FDI and DDI on the export structure in time series framework, utilizing data on FDI inflows to Indonesia and export data based on product stage over 1992–2017. The export structure is analyzed based on three categories, namely primary product, intermediate product, and final product. Our results show that domestic investment has a negative impact on the primary export product, while foreign investment has a positive impact on the final export product. The result highlights the importance of domestic and foreign investment in export upgrading.

scholarly journals Effects of COVID-19 on trade flows: Measuring their impact through government policy responses

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258356
Author(s):  
Javier Barbero ◽  
Juan José de Lucio ◽  
Ernesto Rodríguez-Crespo
Keyword(s):  
Bilateral Trade ◽  
State Of The Art ◽  
Negative Impact ◽  
Trade Agreements ◽  
Trade Flows ◽  
Policy Responses ◽  
Negative Effect ◽  
Bilateral Trade Flows ◽  
Gravity Model Of Trade ◽  
The Impact

This paper examines the impact of COVID-19 on bilateral trade flows using a state-of-the-art gravity model of trade. Using the monthly trade data of 68 countries exporting across 222 destinations between January 2019 and October 2020, our results are threefold. First, we find a greater negative impact of COVID-19 on bilateral trade for those countries that were members of regional trade agreements before the pandemic. Second, we find that the impact of COVID-19 is negative and significant when we consider indicators related to governmental actions. Finally, this negative effect is more intense when exporter and importer country share identical income levels. In the latter case, the highest negative impact is found for exports between high-income countries.

scholarly journals Energy Losses Due to Imperfect Payment Infrastructure and Payment Instruments

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8213
Author(s):  
Oleksandr Melnychenko
Keyword(s):  
Energy Efficiency ◽  
Energy Cost ◽  
Negative Impact ◽  
The European Union ◽  
Strategic Objectives ◽  
Improve Energy Efficiency ◽  
Cost Of Energy ◽  
Using Data ◽  
The Impact ◽  
Number Of Customers

One of the strategic objectives of the European Union is a reduction in greenhouse gas emissions and improvement of energy efficiency by at least 32.5% in different areas of the economy by 2030. However, little is known about the impact of payment in retail on energy consumption. The purpose of this paper is to assess the chain of losses of time and energy, and therefore financial losses, that occur due to the imperfection of payment infrastructure and instruments using data of cashiers’ working time. The research is based on a regression analysis method, where the energy cost per payment transaction is considered in this study as a function of the number of customers per hour and the energy cost. The results of the panel models highlight that the number of customers per hour has a negative impact on the cost of energy per payment transaction. Furthermore, modern means and methods of payment, including cryptocurrencies, do not solve the problem of the excessive time that it takes to service payments, which entails a waste of energy and money. The empirical results give valuable insights into how to best organise payment in retail to achieve lower energy costs and improve energy efficiency in payment infrastructure.

scholarly journals Impacts of Renewable Energy Resources on Effectiveness of Grid-Integrated Systems: Succinct Review of Current Challenges and Potential Solution Strategies

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4856
Author(s):  
Joseph Oyekale ◽  
Mario Petrollese ◽  
Vittorio Tola ◽  
Giorgio Cau
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Renewable Resources ◽  
State Of The Art ◽  
Energy Systems ◽  
Grid Integration ◽  
Renewable Energy Resources ◽  
Solution Strategies ◽  
Review Study ◽  
Renewable Hydrogen

This study is aimed at a succinct review of practical impacts of grid integration of renewable energy systems on effectiveness of power networks, as well as often employed state-of-the-art solution strategies. The renewable energy resources focused on include solar energy, wind energy, biomass energy and geothermal energy, as well as renewable hydrogen/fuel cells, which, although not classified purely as renewable resources, are a famous energy carrier vital for future energy sustainability. Although several world energy outlooks have suggested that the renewable resources available worldwide are sufficient to satisfy global energy needs in multiples of thousands, the different challenges often associated with practical exploitation have made this assertion an illusion to date. Thus, more research efforts are required to synthesize the nature of these challenges as well as viable solution strategies, hence, the need for this review study. First, brief overviews are provided for each of the studied renewable energy sources. Next, challenges and solution strategies associated with each of them at generation phase are discussed, with reference to power grid integration. Thereafter, challenges and common solution strategies at the grid/electrical interface are discussed for each of the renewable resources. Finally, expert opinions are provided, comprising a number of aphorisms deducible from the review study, which reveal knowledge gaps in the field and potential roadmap for future research. In particular, these opinions include the essential roles that renewable hydrogen will play in future energy systems; the need for multi-sectoral coupling, specifically by promoting electric vehicle usage and integration with renewable-based power grids; the need for cheaper energy storage devices, attainable possibly by using abandoned electric vehicle batteries for electrical storage, and by further development of advanced thermal energy storage systems (overviews of state-of-the-art thermal and electrochemical energy storage are also provided); amongst others.

scholarly journals Evaluation and Comparison of Random Forest and A-LSTM Networks for Large-scale Winter Wheat Identification

2019 ◽  
Vol 11 (14) ◽  
pp. 1665 ◽  
Author(s):  
Tianle He ◽  
Chuanjie Xie ◽  
Qingsheng Liu ◽  
Shiying Guan ◽  
Gaohuan Liu
Keyword(s):  
Time Series ◽  
Random Forest ◽  
Winter Wheat ◽  
Large Scale ◽  
Short Term Memory ◽  
State Of The Art ◽  
Training Sample ◽  
Central China ◽  
Surface Reflectance ◽  
The Impact

Machine learning comprises a group of powerful state-of-the-art techniques for land cover classification and cropland identification. In this paper, we proposed and evaluated two models based on random forest (RF) and attention-based long short-term memory (A-LSTM) networks that can learn directly from the raw surface reflectance of remote sensing (RS) images for large-scale winter wheat identification in Huanghuaihai Region (North-Central China). We used a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) images over one growing season and the corresponding winter wheat distribution map for the experiments. Each training sample was derived from the raw surface reflectance of MODIS time-series images. Both models achieved state-of-the-art performance in identifying winter wheat, and the F1 scores of RF and A-LSTM were 0.72 and 0.71, respectively. We also analyzed the impact of the pixel-mixing effect. Training with pure-mixed-pixel samples (the training set consists of pure and mixed cells and thus retains the original distribution of data) was more precise than training with only pure-pixel samples (the entire pixel area belongs to one class). We also analyzed the variable importance along the temporal series, and the data acquired in March or April contributed more than the data acquired at other times. Both models could predict winter wheat coverage in past years or in other regions with similar winter wheat growing seasons. The experiments in this paper showed the effectiveness and significance of our methods.

Trend analysis of Icelandic discharge, precipitation and temperature series

2006 ◽  
Vol 37 (4-5) ◽  
pp. 365-376 ◽  
Author(s):  
Jóna Finndís Jónsdóttir ◽  
Páll Jónsson ◽  
Cintia B. Uvo
Keyword(s):  
Time Series ◽  
Kendall Test ◽  
Temperature Series ◽  
Energy Resources ◽  
Renewable Energy Resources ◽  
Energy Research ◽  
Daily Discharge ◽  
Spring Temperatures ◽  
The Impact

This study is a part of a Nordic co-operative research project, Climate and Energy, funded by Nordic Energy Research and the Nordic energy sector. The project has the objective of a comprehensive assessment of the impact of climate change on Nordic renewable energy resources including hydropower, wind power, biofuels and solar energy. In this paper, the long term variability of precipitation, temperature and discharge of Icelandic rivers is analyzed with respect to trends. Trend is tested for two periods: 1941–2002, since the longest Icelandic discharge records reach 60 years back in time, and 1961–2000, so that a larger set of discharge records could be included, as only a few Icelandic discharge records extend more than 40 years back in time. An eventual trend in the time series is analyzed using the Mann–Kendall test. The test is applied to the time series of both annual and seasonal values, and also to the timing and volume of the maximum daily discharge in spring and autumn, respectively. The main conclusions from the study are that, despite significant increase in measured precipitation, discharge in non-glacial rivers has not increased. Meanwhile, spring temperatures have a negative trend and spring floods, therefore, are larger and delayed.

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