scholarly journals Machine Learning Techniques for Improving Self-Consumption in Renewable Energy Communities

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4892 ◽  
Author(s):  
Zacharie De Grève ◽  
Jérémie Bottieau ◽  
David Vangulick ◽  
Aurélien Wautier ◽  
Pierre-David Dapoz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Renewable Energy ◽  
Wind Power ◽  
Electricity Consumption ◽  
Market Mechanism ◽  
Machine Learning Techniques ◽  
Community Members ◽  
Learning Techniques ◽  
The Impact ◽  
Decentralized Market

Renewable Energy Communities consist in an emerging decentralized market mechanism which allows local energy exchanges between end-users, bypassing the traditional wholesale/retail market structure. In that configuration, local consumers and prosumers gather in communities and can either cooperate or compete towards a common objective, such as the minimization of the electricity costs and/or the minimization of greenhouse gas emissions for instance. This paper proposes data analytics modules which aim at helping the community members to schedule the usage of their resources (generation and consumption) in order to minimize their electricity bill. A day-ahead local wind power forecasting algorithm, which relies on state-of-the-art Machine Learning techniques currently used in worldwide forecasting contests, is in that way proposed. We develop furthermore an original method to improve the performance of neural network forecasting models in presence of abnormal wind power data. A technique for computing representative profiles of the community members electricity consumption is also presented. The proposed techniques are tested and deployed operationally on a pilot Renewable Energy Community established on an Medium Voltage network in Belgium, involving 2.25MW of wind and 18 Small and Medium Enterprises who had the possibility to freely access the results of the developed data modules by connecting to a dedicated web platform. We first show that our method for dealing with abnormal wind power data improves the forecasting accuracy by 10% in terms of Root Mean Square Error. The impact of the developed data modules on the consumption behaviour of the community members is then quantified, by analyzing the evolution of their monthly self-consumption and self-sufficiency during the pilot. No significant changes in the members behaviour, in relation with the information provided by the models, were observed in the recorded data. The pilot was however perturbed by the COVID-19 crisis which had a significant impact on the economic activity of the involved companies. We conclude by providing recommendations for the future set up of similar communities.

scholarly journals Machine learning analysis of lifeguard flag decisions and recorded rescues

2019 ◽  
Vol 19 (11) ◽  
pp. 2541-2549
Author(s):  
Chris Houser ◽  
Jacob Lehner ◽  
Nathan Cherry ◽  
Phil Wernette
Keyword(s):  
Machine Learning ◽  
The United States ◽  
Machine Learning Techniques ◽  
Public Health Issue ◽  
Rip Currents ◽  
Effective Strategies ◽  
Wave Forcing ◽  
Learning Techniques ◽  
Yellow Flag ◽  
The Impact

Abstract. Rip currents and other surf hazards are an emerging public health issue globally. Lifeguards, warning flags, and signs are important, and to varying degrees they are effective strategies to minimize risk to beach users. In the United States and other jurisdictions around the world, lifeguards use coloured flags (green, yellow, and red) to indicate whether the danger posed by the surf and rip hazard is low, moderate, or high respectively. The choice of flag depends on the lifeguard(s) monitoring the changing surf conditions along the beach and over the course of the day using both regional surf forecasts and careful observation. There is a potential that the chosen flag is not consistent with the beach user perception of the risk, which may increase the potential for rescues or drownings. In this study, machine learning is used to determine the potential for error in the flags used at Pensacola Beach and the impact of that error on the number of rescues. Results of a decision tree analysis indicate that the colour flag chosen by the lifeguards was different from what the model predicted for 35 % of days between 2004 and 2008 (n=396/1125). Days when there is a difference between the predicted and posted flag colour represent only 17 % of all rescue days, but those days are associated with ∼60 % of all rescues between 2004 and 2008. Further analysis reveals that the largest number of rescue days and total number of rescues are associated with days where the flag deployed over-estimated the surf and hazard risk, such as a red or yellow flag flying when the model predicted a green flag would be more appropriate based on the wind and wave forcing alone. While it is possible that the lifeguards were overly cautious, it is argued that they most likely identified a rip forced by a transverse-bar and rip morphology common at the study site. Regardless, the results suggest that beach users may be discounting lifeguard warnings if the flag colour is not consistent with how they perceive the surf hazard or the regional forecast. Results suggest that machine learning techniques have the potential to support lifeguards and thereby reduce the number of rescues and drownings.

scholarly journals Monitoring the Impact of Air Quality on the COVID-19 Fatalities in Delhi, India: Using Machine Learning Techniques

2020 ◽  
pp. 1-8
Author(s):  
Jasleen Kaur Sethi ◽  
Mamta Mittal
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Air Pollutants ◽  
Machine Learning Techniques ◽  
Environmental Restoration ◽  
The Novel ◽  
Ozone Pollution ◽  
Learning Techniques ◽  
Novel Coronavirus ◽  
The Impact

ABSTRACT Objective: The focus of this study is to monitor the effect of lockdown on the various air pollutants due to the coronavirus disease (COVID-19) pandemic and identify the ones that affect COVID-19 fatalities so that measures to control the pollution could be enforced. Methods: Various machine learning techniques: Decision Trees, Linear Regression, and Random Forest have been applied to correlate air pollutants and COVID-19 fatalities in Delhi. Furthermore, a comparison between the concentration of various air pollutants and the air quality index during the lockdown period and last two years, 2018 and 2019, has been presented. Results: From the experimental work, it has been observed that the pollutants ozone and toluene have increased during the lockdown period. It has also been deduced that the pollutants that may impact the mortalities due to COVID-19 are ozone, NH3, NO2, and PM10. Conclusions: The novel coronavirus has led to environmental restoration due to lockdown. However, there is a need to impose measures to control ozone pollution, as there has been a significant increase in its concentration and it also impacts the COVID-19 mortality rate.

scholarly journals Iterative machine learning applied to annotation of text datasets

2021 ◽  
Author(s):  
Thiago Abdo ◽  
Fabiano Silva
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Computational Cost ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Learning Techniques ◽  
The Creation ◽  
The Impact ◽  
High Computational Cost

The purpose of this paper is to analyze the use of different machine learning approaches and algorithms to be integrated as an automated assistance on a tool to aid the creation of new annotated datasets. We evaluate how they scale in an environment without dedicated machine learning hardware. In particular, we study the impact over a dataset with few examples and one that is being constructed. We experiment using deep learning algorithms (Bert) and classical learning algorithms with a lower computational cost (W2V and Glove combined with RF and SVM). Our experiments show that deep learning algorithms have a performance advantage over classical techniques. However, deep learning algorithms have a high computational cost, making them inadequate to an environment with reduced hardware resources. Simulations using Active and Iterative machine learning techniques to assist the creation of new datasets are conducted. For these simulations, we use the classical learning algorithms because of their computational cost. The knowledge gathered with our experimental evaluation aims to support the creation of a tool for building new text datasets.

scholarly journals Machine learning approach for simulation of heavy metal concentration in river water: the Crimean peninsula case study

2020 ◽  
Vol 163 ◽  
pp. 06009
Author(s):  
Evgeniy Malygin ◽  
Mikhail Lychagin
Keyword(s):  
Machine Learning ◽  
Heavy Metal ◽  
Metal Concentration ◽  
Heavy Metal Concentration ◽  
Machine Learning Techniques ◽  
Crimean Peninsula ◽  
River Waters ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
The Impact

This study proposes an approach for simulation of heavy metal concentration in river waters using machine learning techniques. A regression model was built and it captured the relationship between the concentration of heavy metal and metalloids (HMM) and several characteristics of studied catchment. Machine learning techniques allowed to simulate the annual concentration variability of HMM. This approach allows exploring the impact of different factors on studied processes.

Automatic Routing in Triage Process

2013 ◽  
Author(s):  
Dinesh Rathi
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Domain Experts ◽  
Digital Reference ◽  
Learning Techniques ◽  
The Impact

This study investigates and characterizes the impact of different features of email on effective routing of email to domain experts. The findings of the study would help in understanding how machine learning techniques such as classification could be applied effectively to develop better automatic triage process in digital reference service.Cette étude examine et caractérise l'impact de différentes caractéristiques des courriels sur leur acheminement efficace aux experts du domaine. Les résultats de l'étude permettraient de comprendre comment les techniques d'apprentissages machine comme la classification pourraient être appliquées efficacement afin de développer de meilleurs processus de triage automatique pour les services de référence numérique. 

Monitoring Effects of Drought on Nitrogen and Phosphorus in Temperate Oak Forests Using Machine Learning Techniques

2018 ◽  
Author(s):  
Jan Kotlarz ◽  
Katarzyna Kubiak ◽  
Marcin Spiralski
Keyword(s):  
Machine Learning ◽  
Drought Stress ◽  
Negative Impact ◽  
Aerial Images ◽  
Machine Learning Techniques ◽  
Nutrient Concentrations ◽  
Nitrogen And Phosphorus ◽  
Learning Techniques ◽  
The Impact

Oak is a European tree species highly sensitive to drought. If declining symptoms appear they are often detectable at the crown (such as dieback) enabling monitoring using aerial images and remote sensing methods. Here, we analyzed the impact of short and long-term drought on oaks located in central Poland, between the years of 2014 and 2017. We used leaf nitrogen (N) and phosphorus (P) concentrations measured in the laboratory, aerial images collected in the range of 460-880 nm and machine learning techniques to estimate nutrient concentrations on the > 4000 oaks growing on gleysoil in the study area. We determined a negative impact on N and P concentrations during both types of drought stress (-23% and 19% for N concentration in leaves; -27% and -10% for P concentration in leaves) and an inconsiderable impact on N:P values (3% increase of N:P ration during short and 7% decrease of N:P ration during long-term drought stress). We found that the long-term drought impact was spatially diverse, possibly depending on the presence of drainage ditches and competing species.

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