scholarly journals Short Term Residential Load Forecasting: An Improved Optimal Nonlinear Auto Regressive (NARX) Method with Exponential Weight Decay Function

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 432 ◽  
Author(s):  
Farukh Abbas ◽  
Donghan Feng ◽  
Salman Habib ◽  
Usama Rahman ◽  
Aazim Rasool ◽  
...  
Keyword(s):  
Neural Network ◽  
Planning Process ◽  
Search Algorithm ◽  
Load Forecasting ◽  
Open Loop ◽  
Percentage Error ◽  
Computational Techniques ◽  
Loop Model ◽  
Exponential Weight ◽  
Weight Decay

The advancement in electrical load forecasting techniques with new algorithms offers reliable solutions to operators for operational cost reduction, optimum use of available resources, effective power management, and a reliable planning process. The focus is to develop a comprehensive understanding regarding the forecast accuracy generated by employing a state of the art optimal autoregressive neural network (NARX) for multiple, nonlinear, dynamic, and exogenous time varying input vectors. Other classical computational methods such as a bagged regression tree (BRT), an autoregressive and moving average with external inputs (ARMAX), and a conventional feedforward artificial neural network are implemented for comparative error assessment. The training of the applied method is realized in a closed loop by feeding back the predicted results obtained from the open loop model, which made the implemented model more robust when compared with conventional forecasting approaches. The recurrent nature of the applied model reduces its dependency on the external data and a produced mean absolute percentage error (MAPE) below 1%. Subsequently, more precision in handling daily grid operations with an average improvement of 16%–20% in comparison with existing computational techniques is achieved. The network is further improved by proposing a lightning search algorithm (LSA) for optimized NARX network parameters and an exponential weight decay (EWD) technique to control the input error weights.

scholarly journals A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Lizhen Wu ◽  
Chun Kong ◽  
Xiaohong Hao ◽  
Wei Chen
Keyword(s):  
Neural Network ◽  
Power System ◽  
Network Model ◽  
Neural Network Model ◽  
Feature Vector ◽  
Load Forecasting ◽  
Percentage Error ◽  
Short Term ◽  
Hybrid Neural Network ◽  
Short Term Load Forecasting

Short-term load forecasting (STLF) plays a very important role in improving the economy and stability of the power system operation. With the smart meters and smart sensors widely deployed in the power system, a large amount of data was generated but not fully utilized, these data are complex and diverse, and most of the STLF methods cannot well handle such a huge, complex, and diverse data. For better accuracy of STLF, a GRU-CNN hybrid neural network model which combines the gated recurrent unit (GRU) and convolutional neural networks (CNN) was proposed; the feature vector of time sequence data is extracted by the GRU module, and the feature vector of other high-dimensional data is extracted by the CNN module. The proposed model was tested in a real-world experiment, and the mean absolute percentage error (MAPE) and the root mean square error (RMSE) of the GRU-CNN model are the lowest among BPNN, GRU, and CNN forecasting methods; the proposed GRU-CNN model can more fully use data and achieve more accurate short-term load forecasting.

scholarly journals Ensemble Neural Network Approach for Accurate Load Forecasting in a Power System

2009 ◽  
Vol 19 (2) ◽  
pp. 303-315 ◽  
Author(s):  
Krzysztof Siwek ◽  
Stanisław Osowski ◽  
Ryszard Szupiluk
Keyword(s):  
Neural Network ◽  
Power System ◽  
Mean Squared Error ◽  
Load Forecasting ◽  
Weighted Averaging ◽  
Percentage Error ◽  
Network Approach ◽  
Three Solutions ◽  
Neural Network Approach ◽  
Ensemble Neural Network

Ensemble Neural Network Approach for Accurate Load Forecasting in a Power SystemThe paper presents an improved method for 1-24 hours load forecasting in the power system, integrating and combining different neural forecasting results by an ensemble system. We will integrate the results of partial predictions made by three solutions, out of which one relies on a multilayer perceptron and two others on self-organizing networks of the competitive type. As the expert system we will apply different integration methods: simple averaging, SVD based weighted averaging, principal component analysis and blind source separation. The results of numerical experiments, concerning forecasting the hourly load for the next 24 hours of the Polish power system, will be presented and discussed. We will compare the performance of different ensemble methods on the basis of the mean absolute percentage error, mean squared error and maximum percentage error. They show a significant improvement of the proposed ensemble method in comparison to the individual results of prediction. The comparison of our work with the results of other papers for the same data proves the superiority of our approach.

scholarly journals Short Term Load Forecasting Based Artificial Neural Network

2014 ◽  
Vol 10 (1) ◽  
pp. 42-47
Author(s):  
Adel Dakhil
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Load Forecasting ◽  
Percentage Error ◽  
Short Term ◽  
Historical Series ◽  
Feed Forward Network ◽  
Artificial Neural ◽  
Input Variables ◽  
Analysis System

Present study develops short term electric load forecasting using neural network; based on historical series of power demand the neural network chosen for this network is feed forward network, this neural network has five input variables ( hour of the day, the day of the week, the load for the previous hour, the load of the pervious day, the load for the previous week). Short term load forecast is very important due to accurate for power system operation and analysis system security among other mandatory function. The trained artificial neural network shows good accuracy and robust in forecasting future load demands for the daily operation, mean absolute percentage error (MAPE) was calculated and it is maximum value is 0.75% in load forecasting on Monday.

scholarly journals Layer Recurrent Neural Network based Power System Load Forecasting

2015 ◽  
Vol 16 (3) ◽  
pp. 423
Author(s):  
Nikita Mittal ◽  
Akash Saxena
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Load Forecasting ◽  
Absolute Error ◽  
Percentage Error ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Load Pattern ◽  
Facility Expansion ◽  
Large Distribution

This paper presents a straight forward application of Layer Recurrent Neural Network (LRNN) to predict the load of a large distribution network. Short term load forecasting provides important information about the system’s load pattern, which is a premier requirement in planning periodical operations and facility expansion. Approximation of data patterns for forecasting is not an easy task to perform. In past, various approaches have been applied for forecasting. In this work application of LRNN is explored. The results of proposed architecture are compared with other conventional topologies of neural networks on the basis of Root Mean Square of Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE). It is observed that the results obtained from LRNN are comparatively more significant.

Analysis and evaluation of two short-term load forecasting techniques

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Saroj Kumar Panda ◽  
Papia Ray
Keyword(s):  
Neural Network ◽  
Power System ◽  
Deep Neural Network ◽  
Load Forecasting ◽  
Percentage Error ◽  
Correlation Technique ◽  
Short Term ◽  
Efficient Operation ◽  
Decision Mechanism ◽  
Short Term Load Forecasting

Abstract Short-term load forecasting (STLF) is very important for an efficient operation of the power system because the exact and stable load forecasting brings good results to the power system. This manuscript presents the application of two new models in STLF i.e. Cross multi-models and second decision mechanism and Residential load forecasting in smart grid using deep neural network models. In the cross multi-model and second decision mechanism method, the horizontal and longitudinal load characteristics are useful for the construction of the model with the calculation of the total load. The dataset for this model is considered from Maine in New England, Singapore, and New South Wales of Australia. While, In the residential load forecasting method, the Spatio-temporal correlation technique is used for the construction of the iterative ResBlock and deep neural network which helps to give the characteristics of residential load with the use of a publicly available Redd dataset. The performances of the proposed models are calculated by the Root Mean Square Error, Mean Absolute Error, and Mean Absolute Percentage Error. From the simulation results, it is concluded that the performance of cross multi-model and second decision mechanism is good as compare to the residential load forecasting.

scholarly journals Daily Peak Load Forecast Using Artificial Neural Network

2019 ◽  
Vol 9 (4) ◽  
pp. 2256
Author(s):  
Ramesh Kumar V ◽  
Pradipkumar Dixit
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Learning Algorithm ◽  
Peak Load ◽  
Load Forecasting ◽  
Percentage Error ◽  
Ann Model ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Artificial Neural

The paper presents an Artificial Neural Network (ANN) model for short-term load forecasting of daily peak load. A multi-layered feed forward neural network with Levenberg-Marquardt learning algorithm is used because of its good generalizing property and robustness in prediction. The input to the network is in terms of historical daily peak load data and corresponding daily peak temperature data. The network is trained to predict the load requirement ahead. The effectiveness of the proposed ANN approach to the short-term load forecasting problems is demonstrated by practical data from the Bangalore Electricity Supply Company Limited (BESCOM). The comparison between the proposed and the conventional methods is made in terms of percentage error and it is found that the proposed ANN model gives more accurate predictions with optimal number of neurons in the hidden layer.

scholarly journals Short Term Load Forecasting of Distribution Feeder Using Artificial Neural Network Technique

2021 ◽  
Vol 2 (1) ◽  
pp. 1-22
Author(s):  
Kumilachew Chane ◽  
◽  
Fsaha Mebrahtu Gebru ◽  
Baseem Khan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Load Forecasting ◽  
Electric Utility ◽  
Percentage Error ◽  
Electrical Load ◽  
Short Term ◽  
Load Demand ◽  
Short Term Load Forecasting ◽  
Artificial Neural

This paper explains the load forecasting technique for prediction of electrical load at Hawassa city. In a deregulated market it is much need for a generating company to know about the market load demand for generating near to accurate power. If the generation is not sufficient to fulfill the demand, there would be problem of irregular supply and in case of excess generation the generating company will have to bear the loss. Neural network techniques have been recently suggested for short-term load forecasting by a large number of researchers. Several models were developed and tested on the real load data of a Finnish electric utility at Hawassa city. The authors carried out short-term load forecasting for Hawassa city using ANN (Artificial Neural Network) technique ANN was implemented on MATLAB and ETAP. Hourly load means the hourly power consumption in Hawassa city. Error was calculated as MAPE (Mean Absolute Percentage Error) and with error of about 1.5296% this paper was successfully carried out. This paper can be implemented by any intensive power consuming town for predicting the future load and would prove to be very useful tool while sanctioning the load.

An Efficient Load Forecasting in Predictive Control Strategy Using Hybrid Neural Network

2019 ◽  
Vol 29 (01) ◽  
pp. 2050010 ◽  
Author(s):  
Shweta Sengar ◽  
Xiaodong Liu
Keyword(s):  
Neural Network ◽  
Search Algorithm ◽  
Load Forecasting ◽  
Cuckoo Search ◽  
Forecasting Model ◽  
Lévy Flight ◽  
Hybrid Neural Network ◽  
Levy Flight ◽  
Power Load ◽  
Power Load Forecasting

Load forecasting is a difficult task, because the load series is complex and exhibits several levels of seasonality. The load at a given hour is dependent not only on the load at the previous day, but also on the load at the same hour on the previous day and previous week, and because there are many important exogenous variables that must be considered. Most of the researches were simultaneously concentrated on the number of input variables to be considered for the load forecasting problem. In this paper, we concentrate on optimizing the load demand using forecasting of the weather conditions, water consumption, and electrical load. Here, the neural network (NN) power load forecasting model clubbed with Levy-flight from cuckoo search algorithm is proposed, i.e., called hybrid neural network (HNN), and named as LF-HNN, where the Levy-flight is used to automatically select the appropriate spread parameter value for the NN power load forecasting model. The results from the simulation work have demonstrated the value of the LF-HNN approach successfully selected the appropriate operating mode to achieve optimization of the overall energy efficiency of the system using all available energy resources.

scholarly journals A short-term load forecasting taking into account the correlation of integrated energy load

2020 ◽  
Vol 185 ◽  
pp. 01009
Author(s):  
Xianjun Qi ◽  
Qinghui Chen ◽  
Xiwei Zheng
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Cross Correlation ◽  
Load Forecasting ◽  
Percentage Error ◽  
Short Term ◽  
Frequency Bands ◽  
Short Term Load Forecasting ◽  
Heating Load ◽  
The Cross

This paper proposes a short-term load forecasting method that takes into account the correlation of integrated energy load. The method use wavelet packet to decompose the electric cooling and heating load in frequency bands, analyze the cross-correlation of the electric cooling and heating load in each frequency band, and choose different forecasting methods according to the strength of the correlation to reflect the cross-correlation of the load itself; the method use recurrent neural network as a forecasting model to reflect the autocorrelation of the load itself. Compared with putting the electric cooling and heating load into the same recurrent neural network or back propagation neural network for forecasting, the method in this paper considers the autocorrelation of the electric cooling and heating load itself and the cross- correlation of the electric cooling and heating load in different frequency bands. This method reduces the average absolute percentage error of the load forecasting.

scholarly journals Fusion of Improved Sparrow Search Algorithm and Long Short-Term Memory Neural Network Application in Load Forecasting

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 130
Author(s):  
Gwo-Ching Liao
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Power Systems ◽  
Short Term Memory ◽  
Search Algorithm ◽  
Load Forecasting ◽  
Short Term ◽  
Term Memory ◽  
Short Term Load Forecasting ◽  
Long Short Term Memory

Load forecasting (LF) is essential in enabling modern power systems’ safety and economical transportation and energy management systems. The dynamic balance between power generation and load in the optimization of power systems is receiving increasing attention. The intellectual development of information in the power industry and the data acquisition system of the smart grid provides a vast data source for pessimistic load forecasting, and it is of great significance in mining the information behind power data. An accurate short-term load forecasting can guarantee a system’s safe and reliable operation, improve the utilization rate of power generation, and avoid the waste of power resources. In this paper, the load forecasting model by applying a fusion of Improved Sparrow Search Algorithm and Long Short-Term Memory Neural Network (ILSTM-NN), and then establish short-term load forecasting using this novel model. Sparrow Search Algorithm is a novel swarm intelligence optimization algorithm that simulates sparrow foraging predatory behavior. It is used to optimize the parameters (such as weight, bias, etc.) of the ILSTM-NN. The results of the actual examples are used to prove the accuracy of load forecasting. It can improve (decrease) the MAPE by about 20% to 50% and RMSE by about 44.1% to 52.1%. Its ability to improve load forecasting error values is tremendous, so it is very suitable for promoting a domestic power system.

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