Short-Term Power Forecasting Framework for Microgrids Using Combined Baseline and Regression Models

Short-term electric power forecasting is a tool of great interest for power systems, where the presence of renewable and distributed generation sources is constantly growing. Specifically, this type of forecasting is essential for energy management systems in buildings, industries and microgrids for optimizing the operation of their distributed energy resources under different criteria based on their expected daily energy balance (the consumption–generation relationship). Under this situation, this paper proposes a complete framework for the short-term multistep forecasting of electric power consumption and generation in smart grids and microgrids. One advantage of the proposed framework is its capability of evaluating numerous combinations of inputs, making it possible to identify the best technique and the best set of inputs in each case. Therefore, even in cases with insufficient input information, the framework can always provide good forecasting results. Particularly, in this paper, the developed framework is used to compare a whole set of rule-based and machine learning techniques (artificial neural networks and random forests) to perform day-ahead forecasting. Moreover, the paper presents and a new approach consisting of the use of baseline models as inputs for machine learning models, and compares it with others. Our results show that this approach can significantly improve upon the compared techniques, achieving an accuracy improvement of up to 62% over that of a persistence model, which is the best of the compared algorithms across all application cases. These results are obtained from the application of the proposed methodology to forecasting five different load and generation power variables for the Savona Campus at the University of Genova in Italy.

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

Energies ◽

10.3390/en14164776 ◽

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.

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Global and Local Clustering-Based Regression Models to Forecast Power Consumption in Buildings

Advances in Computer and Electrical Engineering - Handbook of Research on Emerging Technologies for Electrical Power Planning, Analysis, and Optimization ◽

10.4018/978-1-4666-9911-3.ch011 ◽

2016 ◽

pp. 207-234

Author(s):

Gonzalo Vergara ◽

Juan J. Carrasco ◽

Jesus Martínez-Gómez ◽

Manuel Domínguez ◽

José A. Gámez ◽

...

Keyword(s):

Machine Learning ◽

Power Consumption ◽

Electric Power ◽

Principal Component ◽

Real Data ◽

Machine Learning Techniques ◽

Electric Power Consumption ◽

Support Vector ◽

Learning Machines ◽

The University

The study of energy efficiency in buildings is an active field of research. Modeling and predicting energy related magnitudes leads to analyze electric power consumption and can achieve economical benefits. In this study, classical time series analysis and machine learning techniques, introducing clustering in some models, are applied to predict active power in buildings. The real data acquired corresponds to time, environmental and electrical data of 30 buildings belonging to the University of León (Spain). Firstly, we segmented buildings in terms of their energy consumption using principal component analysis. Afterwards, we applied state of the art machine learning methods and compare between them. Finally, we predicted daily electric power consumption profiles and compare them with actual data for different buildings. Our analysis shows that multilayer perceptrons have the lowest error followed by support vector regression and clustered extreme learning machines. We also analyze daily load profiles on weekdays and weekends for different buildings.

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Global and Local Clustering-Based Regression Models to Forecast Power Consumption in Buildings

Architecture and Design ◽

10.4018/978-1-5225-7314-2.ch018 ◽

2019 ◽

pp. 506-536

Author(s):

Gonzalo Vergara ◽

Juan J. Carrasco ◽

Jesus Martínez-Gómez ◽

Manuel Domínguez ◽

José A. Gámez ◽

...

Keyword(s):

Machine Learning ◽

Power Consumption ◽

Electric Power ◽

Principal Component ◽

Real Data ◽

Machine Learning Techniques ◽

Electric Power Consumption ◽

Support Vector ◽

Learning Machines ◽

The University

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Short term wind power forecasting using machine learning techniques

Journal of Statistics and Management Systems ◽

10.1080/09720510.2020.1721632 ◽

2020 ◽

Vol 23 (1) ◽

pp. 145-156 ◽

Cited By ~ 2

Author(s):

Aditya Chaudhary ◽

Akash Sharma ◽

Ayush Kumar ◽

Karan Dikshit ◽

Neeraj Kumar

Keyword(s):

Machine Learning ◽

Wind Power ◽

Machine Learning Techniques ◽

Short Term ◽

Wind Power Forecasting ◽

Learning Techniques ◽

Power Forecasting

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A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score

Entropy ◽

10.3390/e22121412 ◽

2020 ◽

Vol 22 (12) ◽

pp. 1412

Author(s):

Eliana Vivas ◽

Héctor Allende-Cid ◽

Rodrigo Salas

Keyword(s):

Machine Learning ◽

Power Systems ◽

Electric Power ◽

Electrical Power ◽

Forecast Accuracy ◽

Electric Power System ◽

Forecasting Models ◽

Distribution Of Power ◽

Forecasting Errors ◽

Power Forecasting

Electric power forecasting plays a substantial role in the administration and balance of current power systems. For this reason, accurate predictions of service demands are needed to develop better programming for the generation and distribution of power and to reduce the risk of vulnerabilities in the integration of an electric power system. For the purposes of the current study, a systematic literature review was applied to identify the type of model that has the highest propensity to show precision in the context of electric power forecasting. The state-of-the-art model in accurate electric power forecasting was determined from the results reported in 257 accuracy tests from five geographic regions. Two classes of forecasting models were compared: classical statistical or mathematical (MSC) and machine learning (ML) models. Furthermore, the use of hybrid models that have made significant contributions to electric power forecasting is identified, and a case of study is applied to demonstrate its good performance when compared with traditional models. Among our main findings, we conclude that forecasting errors are minimized by reducing the time horizon, that ML models that consider various sources of exogenous variability tend to have better forecast accuracy, and finally, that the accuracy of the forecasting models has significantly increased over the last five years.

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Using Machine Learning Algorithms on Prediction of Stock Price

Journal of Modeling and Optimization ◽

10.32732/jmo.2020.12.2.84 ◽

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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