Residential Load Time Series Forecasting using ANN and Classical Methods

Chandran, Lekshmi R.; Jayagopal, Nikhil; Lal, Lekshmi Shyma; Narayanan, Chaithanya; Deepak, S.; Harikrishnan,

doi:10.1109/icces51350.2021.9488969

Cited by 14 publications

(4 citation statements)

References 19 publications

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“…This is a 2-15-1 topology feed forward back propagation trained with an ML. In these three models we used and fixed purelin [17] as the activation function of the hidden layer, and we changed that of the output layer in each model. Table 4 shows us the responses of the network studied in relation to these activation functions.…”

Section: Intelligent Model's Number 7 8 Andmentioning

confidence: 99%

Numerical study of the speed’s response of the various intelligent models using the tansig, logsig and purelin activation functions in different layers of artificial neural network

Laabid

Moumen²,

Mansouri³

et al. 2023

IJ-AI

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<p><span lang="EN-US">Today's world is no longer that of yesterday, the pace with which we live and also the speed is enormous and rapid, that overnight we discover the appearance of new technologies and solutions in all the fields, in particular, that of scientific research. Artificial intelligence plays the main role. Predicting the behavior of new materials using artificial neural networks has become a frequently adopted solution by researchers today. The performance of neural networks depends mainly on the activation functions used. This work was designed to mainly study the impact of these functions on the response speed of an artificial neural network in general, and particularly on the model we are working on to predict the thermomechanical behavior of innovative materials. By using TANSIG, PURELIN and LOGSIG in a feed forward back propagation training by Levenberg-Marquardt algorithm, we were able to generate 9 models. For each of these models, we were interested in analyzing the speed’s response of the network and studying its regression. Thus, this work was able to show us that choosing the right neuron activation function from one layer to another can clearly influence the performance of the results. Depending on the problem studied, the desired objective and the chosen architecture, the activation function can radically change the result and provide us with the expected efficiency. </span></p>

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Section: Intelligent Model's Number 7 8 Andmentioning

confidence: 99%

Numerical study of the speed’s response of the various intelligent models using the tansig, logsig and purelin activation functions in different layers of artificial neural network

Laabid

Moumen²,

Mansouri³

et al. 2023

IJ-AI

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show abstract

“…The artificial network model can solve this problem well, and it can predict the nonlinear part of the data, so it is also widely used in the electricity forecasting process [31]. Through experimental comparison, it is found that the artificial neural network model has higher prediction accuracy than the traditional time series or regression model [32], but the accuracy still needs to be optimized. The future research direction is to explore the use of hybrid forecasting methods [33].…”

Section: Literaturementioning

confidence: 99%

Collaborative Energy Price Computing Based on Sarima-Ann and Asymmetric Stackelberg Games

Zhang

Chen

et al. 2023

Symmetry

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The energy trading problem in smart grids has been of great interest. In this paper, we focus on two problems: 1. Energy sellers’ inaccurate grasp of users’ real needs causes information asymmetry in transactions, making it difficult for energy sellers to develop more satisfactory pricing strategies for users based on those real needs. 2. The uneven variation of user demand causes the grid costs to increase. In this paper, we design a collaborative pricing strategy based on the seasonal autoregressive integrated moving average-artificial neural network (Sarima-Ann) and an asymmetric Stackelberg game. Specifically, we propose a dissatisfaction function for users and an incentive function for grid companies to construct a utility function for both parties, which introduces an incentive amount to achieve better results in equilibrating user demand while optimizing the transaction utility. In addition, we constructed a demand fluctuation function based on user demand data and introduced it into the game model to predict the demand by Sarima-Ann, which achieves better prediction accuracy. Finally, through simulation experiments, we demonstrate the effectiveness of our scheme in balancing demand and improving utility, and the superiority of our Sarima-Ann model in terms of forecasting accuracy. Specifically, the peak reduction can reach 94.1% and the total transaction utility increase can reach 4.6 × 107, and better results can be achieved by adjusting the incentive rate. Our Sarima-Ann model improves accuracy by 64.95% over Arima and 64.47% over Sarima under MAE metric evaluation, and also shows superior accuracy under other metrics evaluation.

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“…The suitability of AI techniques in various forecasting applications is explained in [40][41][42]. The superior performance of an artificial neural network (ANN) for forecasting applications, compared to conventional methods, is discussed in [43]. Arias and Bae [44] present a model of the hourly traffic and weather information in South Korea.…”

Section: Literature Reviewmentioning

confidence: 99%

Short-Term Forecasting of Electric Vehicle Load Using Time Series, Machine Learning, and Deep Learning Techniques

Vishnu,

Kaliyaperumal,

Pati

et al. 2023

WEVJ

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Electric vehicles (EVs) are inducing revolutionary developments to the transportation and power sectors. Their innumerable benefits are forcing nations to adopt this sustainable mode of transport. Governments are framing and implementing various green energy policies. Nonetheless, there exist several critical challenges and concerns to be resolved in order to reap the complete benefits of E-mobility. The impacts of unplanned EV charging are a major concern. Accurate EV load forecasting followed by an efficient charge scheduling system could, to a large extent, solve this problem. This work focuses on short-term EV demand forecasting using three learning frameworks, which were applied to real-time adaptive charging network (ACN) data, and performance was analyzed. Auto-regressive (AR) forecasting, support vector regression (SVR), and long short-term memory (LSTM) frameworks demonstrated good performance in EV charging demand forecasting. Among these, LSTM showed the best performance with a mean absolute error (MAE) of 4 kW and a root-mean-squared error (RMSE) of 5.9 kW.

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Residential Load Time Series Forecasting using ANN and Classical Methods

Cited by 14 publications

References 19 publications

Numerical study of the speed’s response of the various intelligent models using the tansig, logsig and purelin activation functions in different layers of artificial neural network

Numerical study of the speed’s response of the various intelligent models using the tansig, logsig and purelin activation functions in different layers of artificial neural network

Collaborative Energy Price Computing Based on Sarima-Ann and Asymmetric Stackelberg Games

Short-Term Forecasting of Electric Vehicle Load Using Time Series, Machine Learning, and Deep Learning Techniques

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