Short‐term electric power load forecasting using factor analysis and long short‐term memory for smart cities

Veeramsetty, Venkataramana; Chandra, D. Rakesh; Salkuti, Surender Reddy

doi:10.1002/cta.2928

Cited by 32 publications

(16 citation statements)

References 30 publications

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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%

Weather Forecasting Using Radial Basis Function Neural Network in Warangal, India

Veeramsetty¹,

Kiran²,

Sushma³

et al. 2023

Urban Science

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Weather forecasting is an essential task in any region of the world for proper planning of various sectors that are affected by climate change. In Warangal, most sectors, such as agriculture and electricity, are mainly influenced by climate conditions. In this study, weather (WX) in the Warangal region was forecast in terms of temperature and humidity. A radial basis function neural network was used in this study to forecast humidity and temperature. Humidity and temperature data were collected for the period of January 2021 to December 2021. Based on the simulation results, it is observed that the radial basis function neural network model performs better than other machine learning models when forecasting temperature and humidity.

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Section: Radial Basis Function Neural Network (Rbfnn)mentioning

confidence: 99%

Weather Forecasting Using Radial Basis Function Neural Network in Warangal, India

Veeramsetty¹,

Kiran²,

Sushma³

et al. 2023

Urban Science

Self Cite

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“…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%

Zero-Crossing Point Detection of Sinusoidal Signal in Presence of Noise and Harmonics Using Deep Neural Networks

Veeramsetty

Edudodla²,

Salkuti

2021

Algorithms

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Zero-crossing point detection is necessary to establish a consistent performance in various power system applications, such as grid synchronization, power conversion and switch-gear protection. In this paper, zero-crossing points of a sinusoidal signal are detected using deep neural networks. In order to train and evaluate the deep neural network model, new datasets for sinusoidal signals having noise levels from 5% to 50% and harmonic distortion from 10% to 50% are developed. This complete study is implemented in Google Colab using deep learning framework Keras. Results shows that the proposed deep learning model is able to detect zero-crossing points in a distorted sinusoidal signal with good accuracy.

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“…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%

A weighted plain Bayesian algorithm-based method for automatic scheduling of short-term load resources in power grids

Ding

Wang

et al. 2023

International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022)

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Load forecasting is the use of historical data to build a model based on the operating characteristics of the power system, economic conditions, social conditions and natural conditions, and the use of this model to make reliable estimates of the trend of load changes in future periods. Load management is of great importance to the development of many tasks in the power system. Not only does it require comprehensive consideration of many aspects of power dispatch control, operation planning and marketing operations, but it also requires scientific forecasting of power loads in conjunction with actual practice. Short-term load forecasting is an important basis for the power supply company's scheduling plan, helping the staff to monitor the working status of the power system in real time, and is of great significance to the power supply company in reducing costs and the power plant in making power generation plans. However, the load forecasting models established by current research workers do not meet the more stringent forecasting requirements of power supply companies in terms of both accuracy and stability. The main objective of this paper is to investigate the automatic scheduling of short-term load resources for power grids based on a weighted plain Bayesian algorithm. Based on the periodicity and regularity of the historical load, this paper divides the fluctuating intervals of the sequence according to the trend of load changes, extracts the characteristics of the fluctuating intervals to represent the load intervals as electricity consumption patterns and performs density clustering. Based on the clustering results, similar load sequences are selected from the aggregated clusters to determine the reference sequences, and then a load forecasting model is established for load forecasting by combining time series and regression ideas to achieve ultra-short-term load forecasting. The test results show that the method has a good forecasting effect and provides a forecasting method based on electricity consumption patterns for power management departments.

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Short‐term electric power load forecasting using factor analysis and long short‐term memory for smart cities

Cited by 32 publications

References 30 publications

Weather Forecasting Using Radial Basis Function Neural Network in Warangal, India

Weather Forecasting Using Radial Basis Function Neural Network in Warangal, India

Zero-Crossing Point Detection of Sinusoidal Signal in Presence of Noise and Harmonics Using Deep Neural Networks

A weighted plain Bayesian algorithm-based method for automatic scheduling of short-term load resources in power grids

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