Machine Learning Techniques for Supporting Renewable Energy Generation and Integration: A Survey

2014 ◽  
pp. 81-96 ◽  
Author(s):  
Kasun S. Perera ◽  
Zeyar Aung ◽  
Wei Lee Woon
Keyword(s):  
Machine Learning ◽  
Renewable Energy ◽  
Energy Generation ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Renewable Energy Generation

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.

scholarly journals A Gradient Boosting Model Optimized by a Genetic Algorithm for Short-term Riverflow Forecast

2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Tales Lima Fonseca ◽  
Yulia Gorodetskaya ◽  
Gisele Goulart Tavares ◽  
Celso Bandeira de Melo Ribeiro ◽  
Leonardo Goliatt da Fonseca
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Predictive Performance ◽  
Energy Generation ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Short Term ◽  
Learning Techniques ◽  
Hydrometeorological Parameters ◽  
Paraíba Do Sul River

The short-term streamflow forecast is an important parameter in studies related to energy generation and the prediction of possible floods. Flowing through three Brazilian states, the Paraíba do Sul river is responsible for the supply and energy generation in several municipalities.  Machine learning techniques have been studied with the aim of improving these predictions through the use of hydrological and hydrometeorological parameters. Furthermore, the predictive performance of the machine learning techniques are directly related to the quality of the training base and, moreover, to the set of hyperparameters used. The present study explores the combination of the Gradient Boosting technique coupled with a Genetic Algorithm to found the best set of hyperparameter to maximize the predicting performance of the Paraíba do Sul river streamflow.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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