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

Entropy ◽  
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.

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

2021 ◽  
Vol 11 (14) ◽  
pp. 6420
Author(s):  
Antonio Parejo ◽  
Stefano Bracco ◽  
Enrique Personal ◽  
Diego Francisco Larios ◽  
Federico Delfino ◽  
...  
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
Electric Power ◽  
Smart Grids ◽  
Machine Learning Techniques ◽  
Short Term ◽  
Energy Management Systems ◽  
The University ◽  
Persistence Model ◽  
Power Forecasting

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.

scholarly journals A Novel Methodology for Adaptive Coordination of Multiple Controllers in Electrical Grids

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1474
Author(s):  
Ruben Tapia-Olvera ◽  
Francisco Beltran-Carbajal ◽  
Antonio Valderrabano-Gonzalez ◽  
Omar Aguilar-Mejia
Keyword(s):  
Power Systems ◽  
Power System ◽  
Electric Power ◽  
Large Scale ◽  
Short Circuit ◽  
Dynamic Performance ◽  
Low Frequency ◽  
Electric Power System ◽  
Design Stage ◽  
Positive Interaction

This proposal is aimed to overcome the problem that arises when diverse regulation devices and controlling strategies are involved in electric power systems regulation design. When new devices are included in electric power system after the topology and regulation goals were defined, a new design stage is generally needed to obtain the desired outputs. Moreover, if the initial design is based on a linearized model around an equilibrium point, the new conditions might degrade the whole performance of the system. Our proposal demonstrates that the power system performance can be guaranteed with one design stage when an adequate adaptive scheme is updating some critic controllers’ gains. For large-scale power systems, this feature is illustrated with the use of time domain simulations, showing the dynamic behavior of the significant variables. The transient response is enhanced in terms of maximum overshoot and settling time. This is demonstrated using the deviation between the behavior of some important variables with StatCom, but without or with PSS. A B-Spline neural networks algorithm is used to define the best controllers’ gains to efficiently attenuate low frequency oscillations when a short circuit event is presented. This strategy avoids the parameters and power system model dependency; only a dataset of typical variable measurements is required to achieve the expected behavior. The inclusion of PSS and StatCom with positive interaction, enhances the dynamic performance of the system while illustrating the ability of the strategy in adding different controllers in only one design stage.

BASIN CONFIGURATION OF A SIX-DIMENSIONAL MODEL OF AN ELECTRIC POWER SYSTEM

2002 ◽  
Vol 12 (06) ◽  
pp. 1333-1356 ◽  
Author(s):  
YOSHISUKE UEDA ◽  
HIROYUKI AMANO ◽  
RALPH H. ABRAHAM ◽  
H. BRUCE STEWART
Keyword(s):  
Power Systems ◽  
Power System ◽  
Initial Conditions ◽  
Electrical Power ◽  
Practical Importance ◽  
Intervention Strategy ◽  
Basins Of Attraction ◽  
Electric Power System ◽  
Point Of View ◽  
Electrical Power System

As part of an ongoing project on the stability of massively complex electrical power systems, we discuss the global geometric structure of contacts among the basins of attraction of a six-dimensional dynamical system. This system represents a simple model of an electrical power system involving three machines and an infinite bus. Apart from the possible occurrence of attractors representing pathological states, the contacts between the basins have a practical importance, from the point of view of the operation of a real electrical power system. With the aid of a global map of basins, one could hope to design an intervention strategy to boot the power system back into its normal state. Our method involves taking two-dimensional sections of the six-dimensional state space, and then determining the basins directly by numerical simulation from a dense grid of initial conditions. The relations among all the basins are given for a specific numerical example, that is, choosing particular values for the parameters in our model.

scholarly journals Adaptive Solar Power Forecasting based on Machine Learning Methods

2018 ◽  
Vol 8 (11) ◽  
pp. 2224 ◽  
Author(s):  
Yu Wang ◽  
Hualei Zou ◽  
Xin Chen ◽  
Fanghua Zhang ◽  
Jie Chen
Keyword(s):  
Power Systems ◽  
Solar Power ◽  
Machine Learning Methods ◽  
Load Demand ◽  
Forecasting Method ◽  
Combining Data ◽  
Data Updating ◽  
Solar Power Forecasting ◽  
Forecasting Errors ◽  
Power Forecasting

Due to the existence of predicting errors in the power systems, such as solar power, wind power and load demand, the economic performance of power systems can be weakened accordingly. In this paper, we propose an adaptive solar power forecasting (ASPF) method for precise solar power forecasting, which captures the characteristics of forecasting errors and revises the predictions accordingly by combining data clustering, variable selection, and neural network. The proposed ASPF is thus quite general, and does not require any specific original forecasting method. We first propose the framework of ASPF, featuring the data identification and data updating. We then present the applied improved k-means clustering, the least angular regression algorithm, and BPNN, followed by the realization of ASPF, which is shown to improve as more data collected. Simulation results show the effectiveness of the proposed ASPF based on the trace-driven data.

A Review of the Smart Grid Concept for Electrical Power System

2022 ◽  
pp. 1361-1385
Author(s):  
Amam Hossain Bagdadee ◽  
Li Zhang
Keyword(s):  
Smart Grid ◽  
Power Systems ◽  
Power System ◽  
Electric Power ◽  
Smart Grids ◽  
Electrical Power ◽  
Future Generation ◽  
Electric Power Systems ◽  
Basic Information ◽  
Electrical Power System

The review this article conducts is an extensive analysis of the concept of a smart grid framework with the most sophisticated smart grid innovation and some basic information about smart grid soundness. Smart grids as a new scheme for energy and a future generation framework encourages the expansion of information and progress. The smart grid framework concord will potentially take years. In this article, the focus is on developing smart networks within the framework of electric power systems.

Integral Models of Developing Electric Power Systems

2013 ◽  
Vol 2 (4) ◽  
pp. 44-58 ◽  
Author(s):  
E. V. Markova ◽  
I. V. Sidler ◽  
V. V. Trufanov
Keyword(s):  
Power Systems ◽  
Power System ◽  
Electric Power ◽  
System Development ◽  
Electric Power Industry ◽  
Electric Power System ◽  
Integral Model ◽  
Volterra Equations ◽  
Prony Method ◽  
Optimal Service

The first part of the paper is devoted to the problem of optimal control in the area of electric power industry which is described on the basis of a one-sector variant of Glushkov integral model of developing systems. The authors consider the ways uncertain conditions of future electric power system development influence the optimal service life. The results of calculations for the Unified Electric Power System of Russia are presented and analyzed. The second part of the paper deals with the application of Prony method to identification of the Volterra equations in the two-sector models of developing systems. The authors suggest a numerical method for identifying the efficiency function parameters. An illustrative example is given.

scholarly journals Modeling and simulation of transients in electric power systems using hybrid system theory

2019 ◽  
Vol 24 ◽  
pp. 02012
Author(s):  
Yury Shornikov ◽  
Evgeny Popov
Keyword(s):  
System Theory ◽  
Power Systems ◽  
Power System ◽  
Mathematical Models ◽  
Modeling And Simulation ◽  
Electric Power ◽  
Hybrid System ◽  
Electric Power Systems ◽  
Electric Power System ◽  
Simulation Environment

Transients in electric power systems are of great interest to power engineers when designing a new or maintaining an existing system. The paper deals with using hybrid system theory for modeling and simulation of an electric power system with controllers. The presented technique is rather convenient and recommended as mathematical models of transients in electric power systems with controllers in general contain both continuous and discrete components. The modeling and simulation were carried out in the modeling and simulation environment ISMA, which is briefly presented in the paper.

A Review of the Smart Grid Concept for Electrical Power System

2019 ◽  
Vol 8 (4) ◽  
pp. 105-126
Author(s):  
Amam Hossain Bagdadee ◽  
Li Zhang
Keyword(s):  
Smart Grid ◽  
Power Systems ◽  
Electric Power ◽  
Smart Grids ◽  
Electrical Power ◽  
Future Generation ◽  
Electric Power Systems ◽  
Basic Information ◽  
Electrical Power System ◽  
Extensive Analysis

The review this article conducts is an extensive analysis of the concept of a smart grid framework with the most sophisticated smart grid innovation and some basic information about smart grid soundness. Smart grids as a new scheme for energy and a future generation framework encourages the expansion of information and progress. The smart grid framework concord will potentially take years. In this article, the focus is on developing smart networks within the framework of electric power systems.

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