scholarly journals Short term load forecasting using LSTM ensembled network on utility scale load demand.

2021 ◽  
Author(s):  
Fathun Karim Fattah ◽  
Pritom Mojumder ◽  
Azmol Ahmed Fuad ◽  
Mohiuddin Ahmad ◽  
Eklas hossain
Keyword(s):  
Economic Impact ◽  
Ensemble Learning ◽  
Load Forecasting ◽  
Short Term ◽  
Load Demand ◽  
Short Term Load Forecasting ◽  
Utility Companies ◽  
Utility Scale

This work entails producing load forecasting through lstm and lstm ensembled networks and put up a comparative picture between the two. Our work establishes that lstm ensemble learning can produce a better prediction compared to single lstm networks. We tried to quantify the improvement and assess the economic impact that it can have on the utility companies.

scholarly journals A Comparative Analysis Between LSTM and LSTM Ensembled Strategy for Short-term Load Forecasting

2021 ◽  
Author(s):  
Fathun Fattah ◽  
Pritom Mojumder ◽  
Azmol Ahmed Fuad ◽  
Mohiuddin Ahmad ◽  
Eklas hossain
Keyword(s):  
Comparative Analysis ◽  
Economic Impact ◽  
Ensemble Learning ◽  
Load Forecasting ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Utility Companies

This work entails producing load forecasting through lstm and lstm ensembled networks and put up a comparative picture between the two. Our work establishes that lstm ensemble learning can produce a better prediction compared to single lstm networks. We tried to quantify the improvement and assess the economic impact that it can have on the utility companies.

scholarly journals A Comparative Analysis Between LSTM and LSTM Ensembled Strategy for Short-term Load Forecasting

2021 ◽  
Author(s):  
Fathun Fattah ◽  
Pritom Mojumder ◽  
Azmol Ahmed Fuad ◽  
Mohiuddin Ahmad ◽  
Eklas hossain
Keyword(s):  
Comparative Analysis ◽  
Economic Impact ◽  
Ensemble Learning ◽  
Load Forecasting ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Utility Companies

This work entails producing load forecasting through lstm and lstm ensembled networks and put up a comparative picture between the two. Our work establishes that lstm ensemble learning can produce a better prediction compared to single lstm networks. We tried to quantify the improvement and assess the economic impact that it can have on the utility companies.

An intelligent approach towards very short-term load forecasting

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Uttamarani Pati ◽  
Papia Ray ◽  
Arvind R. Singh
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Load Forecasting ◽  
Dew Point ◽  
Short Term ◽  
Spinning Reserve ◽  
Feature Selection Technique ◽  
Selection Technique ◽  
Load Demand ◽  
Short Term Load Forecasting

Abstract Very short term load forecasting (VSTLF) plays a pivotal role in helping the utility workers make proper decisions regarding generation scheduling, size of spinning reserve, and maintaining equilibrium between the power generated by the utility to fulfil the load demand. However, the development of an effective VSTLF model is challenging in gathering noisy real-time data and complicates features found in load demand variations from time to time. A hybrid approach for VSTLF using an incomplete fuzzy decision system (IFDS) combined with a genetic algorithm (GA) based feature selection technique for load forecasting in an hour ahead format is proposed in this research work. This proposed work aims to determine the load features and eliminate redundant features to form a less complex forecasting model. The proposed method considers the time of the day, temperature, humidity, and dew point as inputs and generates output as forecasted load. The input data and historical load data are collected from the Northern Regional Load Dispatch Centre (NRLDC) New Delhi for December 2009, January 2010 and February 2010. For validation of proposed method efficacy, it’s performance is further compared with other conventional AI techniques like ANN and ANFIS, which are integrated with genetic algorithm-based feature selection technique to boost their performance. These techniques’ accuracy is tested through their mean absolute percentage error (MAPE) and normalized root mean square error (nRMSE) value. Compared to other conventional AI techniques and other methods provided through previous studies, the proposed method is found to have acceptable accuracy for 1 h ahead of electrical load forecasting.

A comparative study of short-term load forecasting methods in distribution network

2021 ◽  
Vol 15 (1) ◽  
pp. 23-35
Author(s):  
Tuan Ho Le ◽  
◽  
Quang Hung Le ◽  
Thanh Hoang Phan
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Arima Model ◽  
Distribution Network ◽  
Load Forecasting ◽  
Short Term ◽  
Load Demand ◽  
Forecasting Methods ◽  
Short Term Load Forecasting

Short-term load forecasting plays an important role in building operation strategies and ensuring reliability of any electric power system. Generally, short-term load forecasting methods can be classified into three main categories: statistical approaches, artificial intelligence based-approaches and hybrid approaches. Each method has its own advantages and shortcomings. Therefore, the primary objective of this paper is to investigate the effectiveness of ARIMA model (e.g., statistical method) and artificial neural network (e.g., artificial intelligence based-method) in short-term load forecasting of distribution network. Firstly, the short-term load demand of Quy Nhon distribution network and short-term load demand of Phu Cat distribution network are analyzed. Secondly, the ARIMA model is applied to predict the load demand of two distribution networks. Thirdly, the artificial neural network is utilized to estimate the load demand of these networks. Finally, the estimated results from two applied methods are conducted for comparative purposes.

scholarly journals Performance Evaluation of Different Membership Function in Fuzzy Logic Based Short-Term Load Forecasting

2021 ◽  
Vol 29 (2) ◽  
Author(s):  
Oladimeji Ibrahim ◽  
Waheed Olaide Owonikoko ◽  
Abubakar Abdulkarim ◽  
Abdulrahman Okino Otuoze ◽  
Mubarak Akorede Afolayan ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Membership Function ◽  
Load Forecasting ◽  
Percentage Error ◽  
Short Term ◽  
Load Forecast ◽  
Utility Company ◽  
Load Demand ◽  
Short Term Load Forecasting ◽  
Fuzzy Logic Technique

A mismatch between utility-scale electricity generation and demand often results in resources and energy wastage that needed to be minimized. Therefore, the utility company needs to be able to accurately forecast load demand as a guide for the planned generation. Short-term load forecast assists the utility company in projecting the future energy demand. The predicted load demand is used to plan ahead for the power to be generated, transmitted, and distributed and which is crucial to power system reliability and economics. Recently, various methods from statistical, artificial intelligence, and hybrid methods have been widely used for load forecasts with each having their merits and drawbacks. This paper investigates the application of the fuzzy logic technique for short-term load forecast of a day ahead load. The developed fuzzy logic model used time, temperature, and historical load data to forecast 24 hours load demand. The fuzzy models were based on both the trapezoidal and triangular membership function (MF) to investigate their accuracy and effectiveness for the load forecast. The obtained low Mean Absolute Percentage Error (MAPE), Mean Forecast Error (MFE), and Mean Absolute Deviation (MAD) values from the forecasted load results showed that both models are suitable for short-term load forecasting, however the trapezoidal MF showed better performance than the triangular MF.

Design of a Fuzzy Logic Controller for Short-Term Load Forecasting With Randomly Varying Load

2021 ◽  
Vol 13 (4) ◽  
pp. 32-49
Author(s):  
D. V. N. Ananth ◽  
Lagudu Venkata Suresh Kumar ◽  
Tulasichandra Sekhar Gorripotu ◽  
Ahmad Taher Azar
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Load Forecasting ◽  
Short Term ◽  
Random Load ◽  
Traditional System ◽  
Energy Management Systems ◽  
Load Demand ◽  
Short Term Load Forecasting ◽  
Changes Over Time

Short-term load forecasting (STLF) is an integral component of energy management systems. In this paper, fuzzy logic-based algorithm is used for short-term load forecasting. The load changes over time and the goal is to satisfy the shift in demand and to maintain a fault as low as possible between the reference and real powers. The error in the load demand in mega-watt (MW) is compared with proposed technique as well as conventional methods. Three cases were investigated in which the load changes were 1) more random in nature, but the variance to the reference was more; 2) the random load changes were simpler, but a little different from the reference; and lastly, 3) the load changing was random, and the reference deviation was maximum. The results are analyzed for different load changes, and the corresponding results are verified using MATLAB. The deviation of the error value in load response is less experienced with a fuzzy logic controller than with a traditional system, and in fewer iterations, the objective function is also achieved.

scholarly journals Very Short-Term Load Forecasting Using Hybrid Algebraic Prediction and Support Vector Regression

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Marlon Capuno ◽  
Jung-Su Kim ◽  
Hwachang Song
Keyword(s):  
Prediction Model ◽  
Support Vector Regression ◽  
Peak Load ◽  
Load Forecasting ◽  
Weather Forecast ◽  
Warning Signal ◽  
Support Vector ◽  
Short Term ◽  
Load Demand ◽  
Short Term Load Forecasting

This paper presents a model for very short-term load forecasting (VSTLF) based on algebraic prediction (AP) using a modified concept of the Hankel rank of a sequence. Moreover, AP is coupled with support vector regression (SVR) to accommodate weather forecast parameters for improved accuracy of a longer prediction horizon; thus, a hybrid model is also proposed. To increase system reliability during peak hours, this prediction model also aims to provide more accurate peak-loading conditions when considerable changes in temperature and humidity happen. The objective of going hybrid is to estimate an increase or decrease on the expected peak load demand by presenting the total MW per Celsius degree change (MW/C°) as criterion for providing a warning signal to system operators to prepare necessary storage facilities and sufficient reserve capacities if urgently needed by the system. The prediction model is applied using actual 2014 load demand of mainland South Korea during the summer months of July to September to demonstrate the performance of the proposed prediction model.

scholarly journals Survey on short-term load forecasting using hybrid neural network techniques

2018 ◽  
Vol 7 (2.8) ◽  
pp. 464
Author(s):  
Shaive Dalela ◽  
Aditya Verma ◽  
A L.Amutha
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Load Forecasting ◽  
Theoretical Data ◽  
Electric Power System ◽  
Support Vector ◽  
Short Term ◽  
Load Demand ◽  
Short Term Load Forecasting ◽  
Artificial Neural

Load forecasting is an issue of great importance for the reliable operation of the electric power system grids. Various forecasting methodologies have been proposed in the international research bibliography, following different models and mathematical approaches. In the current work, several latest methodologies based on artificial neural networks along with other techniques have be discussed, in order to obtain short-term load forecasting. In this paper, approaches taken by different researchers considering different parameters in means of predicting the lease error has been shown.  The paper investigates the application of artificial neural networks (ANN) with fuzzy logic (FL), Genetic Algorithm(GA), Particle Swarm Optimization(PSO) and Support Vector Machines(SVM) as forecasting tools for predicting the load demand in short term category. The extracted outcomes indicate the effectiveness of the proposed method, reducing the relative error between real and theoretical data

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