Optimization of decentralized renewable energy system by weather forecasting and deep machine learning techniques

2016 ◽  
Author(s):  
Tomah Sogabe ◽  
Haruhisa Ichikawa ◽  
Tomah Sogabe ◽  
Katsuyoshi Sakamoto ◽  
Koichi Yamaguchi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Renewable Energy ◽  
Weather Forecasting ◽  
Energy System ◽  
Machine Learning Techniques ◽  
Renewable Energy System ◽  
Learning Techniques ◽  
Decentralized Renewable Energy

scholarly journals Near real-time air quality forecasts using the NASA GEOS model

2021 ◽  
Author(s):  
K. Emma Knowland ◽  
Christoph Keller ◽  
Krzysztof Wargan ◽  
Brad Weir ◽  
Pamela Wales ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Real Time ◽  
Weather Forecasting ◽  
Atmospheric Composition ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Wide Range ◽  
Reactive Trace Gases ◽  
Assimilation System

<p>NASA's Global Modeling and Assimilation Office (GMAO) produces high-resolution global forecasts for weather, aerosols, and air quality. The NASA Global Earth Observing System (GEOS) model has been expanded to provide global near-real-time 5-day forecasts of atmospheric composition at unprecedented horizontal resolution of 0.25 degrees (~25 km). This composition forecast system (GEOS-CF) combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 12) to provide detailed analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). Satellite observations are assimilated into the system for improved representation of weather and smoke. The assimilation system is being expanded to include chemically reactive trace gases. We discuss current capabilities of the GEOS Constituent Data Assimilation System (CoDAS) to improve atmospheric composition modeling and possible future directions, notably incorporating new observations (TROPOMI, geostationary satellites) and machine learning techniques. We show how machine learning techniques can be used to correct for sub-grid-scale variability, which further improves model estimates at a given observation site.</p>

Weather Forecasting using Machine Learning Techniques

2019 ◽  
Author(s):  
Siddharth Singh ◽  
Mayank Kaushik ◽  
Ambuj Gupta ◽  
Anil Kumar Malviya
Keyword(s):  
Machine Learning ◽  
Weather Forecasting ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Forecasting the hydroelectric power generation of GCMs using machine learning techniques and deep learning (Almus Dam, Turkey)

Geofizika ◽  
2021 ◽  
Vol 38 (1) ◽  
pp. 1-14
Author(s):  
Hesham Majed Al Rayess ◽  
Asli Ülke Keskin
Keyword(s):  
Machine Learning ◽  
Power Generation ◽  
Renewable Energy ◽  
Deep Learning ◽  
General Circulation ◽  
Power Grid ◽  
Machine Learning Techniques ◽  
Hydroelectric Power ◽  
Learning Techniques ◽  
Hydroelectric Power Generation

Renewable energy is one of the most important factors for developed and sustainable societies. However, its utilization in electrical power grid systems can be very challenging regarding rates predictably. Renewable energy depends mainly on environmental conditions such as rainfall-runoff ratios and temperature. Because of that, the expected power production heavily fluctuates, which makes the prediction and calculation of feed-in into the power grid very challenging. The accurate forecasting of energy production is a very crucial issue for power management process. This paper presents the results of deploying Machine Learning Techniques in short-term forecasting of the amount of energy produced of General Circulation Models (GCMs) Data by Almus Dam and Hydroelectric Power Plant in Tokat, Turkey. The study demonstrates the use of modeling techniques in hydropower forecasting process using the predicted monthly hydroelectric power generation data of GCMs from 2018 to 2080. Decision Tree, Deep Learning, Generalized Linear, Gradient Boosted Trees and Random Forest models are utilized to forecast the hydropower production. The results show that the correlation value of the gradient boosted trees model equals 0.717, which means that the gradient boosted trees model is the most successful model for the present data. The gradient boosted trees model used in the prediction process for each GCM in each scenario is 4.5 and 8.5. The results show that there are small differences between the models, which means that the predictions are going in similar directions for all these models.

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.

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