2020
DOI: 10.1002/cta.2928
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Short‐term electric power load forecasting using factor analysis and long short‐term memory for smart cities

Abstract: Electric load estimation is an important activity for electrical power system operators to operate the system stably and optimally. This paper develops a machine learning model with a long short-term memory and a factor analysis to predict the load at a specific hour of the day on an electrical power substation. Historical load data from the 33-/11-kV substation near Kakatiya University in Warangal are taken at each hour of the day for the period from September 2018 to November 2018. A new long short-term memo… Show more

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Cited by 32 publications
(16 citation statements)
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“…The complete algorithm to train the RBFNN model using the stochastic gradient descent optimizer [43] is presented in Algorithm 1. The performance of the RBFNN is evaluated in terms of mean square error [44][45][46][47][48], as shown in Equation (4).…”
Section: Radial Basis Function Neural Network (Rbfnn)mentioning
confidence: 99%
“…The complete algorithm to train the RBFNN model using the stochastic gradient descent optimizer [43] is presented in Algorithm 1. The performance of the RBFNN is evaluated in terms of mean square error [44][45][46][47][48], as shown in Equation (4).…”
Section: Radial Basis Function Neural Network (Rbfnn)mentioning
confidence: 99%
“…In the proposed DNN model, the ReLU activation function is used in hidden layers, whereas the sigmoid activation function is used in the output layer. Mathematical modeling of ReLU [22] and sigmoid activation functions [23] are Adam optimizer [24,25] is used to train the DNN model by considering minimization of the binary cross-entropy loss function shown below.…”
Section: Deep Neural Networkmentioning
confidence: 99%
“…How to use the data provided by smart meters to improve short-term load forecasting is a challenging task that will attract a great deal of attention for future research. This paper first introduces the principles of load forecasting, followed by an analysis of the need for load forecasting based on the need to carry out load forecasting work in production life [5][6]; then, a brief introduction to existing methods for load forecasting is given, with a focus on the process of implementation in load forecasting applications; the construction of a forecasting model [7][8], and finally load forecasting to obtain the results of load forecasting.…”
Section: Introductionmentioning
confidence: 99%