Automated generation of models for demand side flexibility using machine learning

2021 ◽  
Vol 1 (1) ◽  
pp. 107-120
Author(s):  
Kevin Förderer ◽  
Veit Hagenmeyer ◽  
Hartmut Schmeck
Keyword(s):  
Machine Learning ◽  
Smart Grids ◽  
Renewable Energy Sources ◽  
Energy System ◽  
Underlying Mechanism ◽  
Machine Learning Techniques ◽  
Demand Side ◽  
Distributed Energy ◽  
Automated Generation ◽  
Learning Techniques

Flexibility in consumption and production provided by distributed energy resources (DERs) is a key to the integration of renewable energy sources into the energy system. However, even for identical DERs, the flexibility can vary widely, based on local constraints and circumstances. Therefore, handcrafting models can be labor-intensive and automating the generation of models could help increasing the volume of controllable flexibility in smart grids. Depending on the underlying mechanism for controlling demand side flexibility, there are various ways how an automation can be achieved. In this paper, we discuss fundamental concepts relevant to the automated generation of models for demand side flexibility, give an overview of different approaches, and point out fundamental differences. The main focus lies on model generation by means of machine learning techniques.

scholarly journals Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4776
Author(s):  
Seyed Mahdi Miraftabzadeh ◽  
Michela Longo ◽  
Federica Foiadelli ◽  
Marco Pasetti ◽  
Raul Igual
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
Smart Grids ◽  
State Of The Art ◽  
Smart Cities ◽  
Power Grids ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
New Research ◽  
Traditional Approaches

The recent advances in computing technologies and the increasing availability of large amounts of data in smart grids and smart cities are generating new research opportunities in the application of Machine Learning (ML) for improving the observability and efficiency of modern power grids. However, as the number and diversity of ML techniques increase, questions arise about their performance and applicability, and on the most suitable ML method depending on the specific application. Trying to answer these questions, this manuscript presents a systematic review of the state-of-the-art studies implementing ML techniques in the context of power systems, with a specific focus on the analysis of power flows, power quality, photovoltaic systems, intelligent transportation, and load forecasting. The survey investigates, for each of the selected topics, the most recent and promising ML techniques proposed by the literature, by highlighting their main characteristics and relevant results. The review revealed that, when compared to traditional approaches, ML algorithms can handle massive quantities of data with high dimensionality, by allowing the identification of hidden characteristics of (even) complex systems. In particular, even though very different techniques can be used for each application, hybrid models generally show better performances when compared to single ML-based models.

scholarly journals Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid

2021 ◽  
Vol 13 (22) ◽  
pp. 12653
Author(s):  
Shahzad Aslam ◽  
Nasir Ayub ◽  
Umer Farooq ◽  
Muhammad Junaid Alvi ◽  
Fahad R. Albogamy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Smart Grids ◽  
Load Forecasting ◽  
Error Rates ◽  
Fine Tuning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Optimization Approach ◽  
Daily Consumption ◽  
Learning Techniques

Medium-term electricity consumption and load forecasting in smart grids is an attractive topic of study, especially using innovative data analysis approaches for future energy consumption trends. Loss of electricity during generation and use is also a problem to be addressed. Both consumers and utilities can benefit from a predictive study of electricity demand and pricing. In this study, we used a new machine learning approach called AdaBoost to identify key features from an ISO-NE dataset that includes daily consumption data over eight years. Moreover, the DT classifier and RF are widely used to extract the best features from the dataset. Moreover, we predicted the electricity load and price using machine learning techniques including support vector machine (SVM) and deep learning techniques such as a convolutional neural network (CNN). Coronavirus herd immunity optimization (CHIO), a novel optimization approach, was used to modify the hyperparameters to increase efficiency, and it used classifiers to improve the performance of our classifier. By adding additional layers to the CNN and fine-tuning its parameters, the probability of overfitting the classifier was reduced. For method validation, we compared our proposed models with several benchmarks. MAE, MAPE, MSE, RMSE, the f1 score, recall, precision, and accuracy were the measures used for performance evaluation. Moreover, seven different forms of statistical analysis were given to show why our proposed approaches are preferable. The proposed CNN-CHIO and SVM techniques had the lowest MAPE error rates of 6% and 8%, respectively, and the highest accuracy rates of 95% and 92%, respectively.

scholarly journals Machine Learning Schemes for Anomaly Detection in Solar Power Plants

2021 ◽  
Author(s):  
Mariam Ibrahim ◽  
Ahmad Alsheikh ◽  
Feras M. Awaysheh ◽  
Mohammad Dahman Alshehri
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Smart Grids ◽  
Power Plants ◽  
Short Term Memory ◽  
Solution Space ◽  
Machine Learning Techniques ◽  
Industrial Growth ◽  
Trade Offs ◽  
Learning Techniques

The rapid industrial growth in solar energy is gaining increasing interest in renewable power from smart grids and plants. Anomaly detection in photovoltaic (PV) systems is a demanding task. In this sense, it is vital to utilize recent advances in machine learning to accurately and timely detect different anomalies and condition monitoring. This paper addresses this issue by evaluating different machine learning techniques and schemes and showing how to apply these approaches to solve anomaly detection and detect faults on photovoltaic components. For this, we apply distinct state-of-the-art machine learning techniques (AutoEncoder Long Short-Term Memory (AE-LSTM), Facebook-Prophet, and Isolation Forest) to detect faults/anomalies and evaluate their performance. These models shall identify the PV system's healthy and abnormal actual behaviors. Our results provide clear insights to make an informed decision, especially with experimental trade-offs for such complex solution space.

scholarly journals Method of monitoring and detection of failures in PV system based on machine learning techniques

2020 ◽  
Author(s):  
Dario Javier Benavides ◽  
Paul Arévalo-Cordero ◽  
Luis G. González ◽  
Luis Hernández-Callejo ◽  
Francisco Jurado ◽  
...  
Keyword(s):  
Machine Learning ◽  
Smart Grids ◽  
Energy Production ◽  
Machine Learning Techniques ◽  
Photovoltaic Systems ◽  
Pv System ◽  
Electricity Grid ◽  
Machine Learning Methods ◽  
Scada System ◽  
Learning Techniques

Machine learning methods have been used to solve complicated practical problems in different areas and are becoming increasingly popular today. The purpose of this article is to evaluate the prediction of the energy production of three different photovoltaic systems and the supervision of measurement sensors, through Machine learning and data mining in response to the behavior of the climatic variables of the place under study. On the other hand, it also includes the implementation of the resulting models in the SCADA system through indicators, which will allow the operator to actively manage the electricity grid. It also offers a strategy in simulation and prediction in real-time of photovoltaic systems and measurement sensors in the concept of smart grids.

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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