Short-term electrical load forecasting through heuristic configuration of regularized deep neural network

Haque, Ashraful; Rahman, Saifur

doi:10.1016/j.asoc.2022.108877

Cited by 54 publications

(18 citation statements)

References 39 publications

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“…Models concerning load forecasts have been developed using deep learning algorithms. In [21], for example, the researchers used the Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), Shallow-ANN, SVR, and LR algorithm to predict the short-term electrical load. The electrical load is predicted for 30 min and 24 h ahead for two commercial buildings in Virginia.…”

Section: Related Workmentioning

confidence: 99%

Short-Term Mosques Load Forecast Using Machine Learning and Meteorological Data

AlRashidi¹

2023

Computer Systems Science and Engineering

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The tendency toward achieving more sustainable and green buildings turned several passive buildings into more dynamic ones. Mosques are the type of buildings that have a unique energy usage pattern. Nevertheless, these types of buildings have minimal consideration in the ongoing energy efficiency applications. This is due to the unpredictability in the electrical consumption of the mosques affecting the stability of the distribution networks. Therefore, this study addresses this issue by developing a framework for a short-term electricity load forecast for a mosque load located in Riyadh, Saudi Arabia. In this study, and by harvesting the load consumption of the mosque and meteorological datasets, the performance of four forecasting algorithms is investigated, namely Artificial Neural Network and Support Vector Regression (SVR) based on three kernel functions: Radial Basis (RB), Polynomial, and Linear. In addition, this research work examines the impact of 13 different combinations of input attributes since selecting the optimal features has a major influence on yielding precise forecasting outcomes. For the mosque load, the (SVR-RB) with eleven features appeared to be the best forecasting model with the lowest forecasting errors metrics giving RMSE, nRMSE, MAE, and nMAE values of 4.207 kW, 2.522%, 2.938 kW, and 1.761%, respectively

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Section: Related Workmentioning

confidence: 99%

Short-Term Mosques Load Forecast Using Machine Learning and Meteorological Data

AlRashidi¹

2023

Computer Systems Science and Engineering

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“…According to the studies, the considered model has a higher rate of convergence, as well as better results of electrical load prediction [9]. In [10], the authors proposed a method for short-term prediction of electrical load using a neural network. The forecast was carried out for a week during four different months, which were represented by different seasons in the country.…”

Section: Introductionmentioning

confidence: 99%

“…The forecast was carried out for a week during four different months, which were represented by different seasons in the country. When assessing the effectiveness of electrical load forecasts, the data obtained were compared with the actual data of smart meters from the power supply [10]. In [11], two short-term deep learning electrical load forecasting models (LSTM, CNN) were developed and tested.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Electrical Load Forecasting Based on Neural Networks

Kuantayev

Bainiyazov²,

Uakhitova³

2023

IOP Conf. Ser.: Earth Environ. Sci.

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The article sets out the use of an artificial neural network for electrical load consumption forecasting. Electrical load prediction is one of the most demanded areas of research in the electric power industry. The two-layer artificial neural network of direct distribution with the number of neurons in the hidden layer equal to 6 was proposed in the article. The following data was taken as an input for forecasting short-term electrical load: electrical load, time, day of the week, temperature, day, weekend and working day code. According to the studies, the value of the mean absolute percentage error was 2.35, using the Bayesian Regularization learning algorithm.

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“…The results revealed that the model achieved a reduction in the forecasting error. The study [14] presented a methodology for short-term commercial building electrical load forecasting using a regularized LSTM-RNN. The study reported that the regularized LSTM-RNN model outperformed other ML algorithms, such as shallow-artificial neural network, SVR, and linear regression.…”

Section: Introductionmentioning

confidence: 99%

Residential Load Forecasting Using Modified Federated Learning Algorithm

Park

Son

2023

IEEE Access

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The feasibility of utilizing electricity consumption data is increasing with the widespread use of advanced metering infrastructure among consumers. In addition, the need for consumer demand management and services is likely to increase further as distributed resources are activated. Load forecasting technology is essential for customer demand management. Forecasting technology using deep learning has been applied extensively in recent years. Deep-learning-based load forecasting requires data for training a forecasting model, and the available data may be limited by the Personal Information Protection Act. Federated learning has emerged as a solution to this problem. However, in federated learning, the global model in the central server is trained by aggregating the local model sent from the client without filtration. This may result in the global model overfitting or failure to converge by aggregating the model that is not trained effectively in the client. In this study, we introduce a modified federated learning algorithm to solve this problem and propose a method to forecast residential loads. The proposed method is analyzed and evaluated experimentally using smart meter data. The experimental results reveal that the proposed method improves forecasting performance and convergence. For the global model, when the number of clients was two, three, four, and five, the forecasting performances were 4. 891, 5.228, 5.488, and 5.633, respectively, on a mean absolute percentage error (MAPE) basis, showing performance improvements of 28.4%, 10.0%, 16.3%, and 5.8%.INDEX TERMS Residential load forecasting, federated learning, deep learning, LSTM.

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Short-term electrical load forecasting through heuristic configuration of regularized deep neural network

Cited by 54 publications

References 39 publications

Short-Term Mosques Load Forecast Using Machine Learning and Meteorological Data

Short-Term Mosques Load Forecast Using Machine Learning and Meteorological Data

Short-Term Electrical Load Forecasting Based on Neural Networks

Residential Load Forecasting Using Modified Federated Learning Algorithm

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