Short Term Electric Power Load Forecasting Using Principal Component Analysis and Recurrent Neural Networks

Veeramsetty, Venkataramana; Chandra, D. Rakesh; Grimaccia, Francesco; Mussetta, Marco

doi:10.3390/forecast4010008

Cited by 27 publications

(9 citation statements)

References 34 publications

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“…In unsupervised learning, we can analyze the commonalities and differences between the studied objects. It mainly addresses two types of problems, clustering and association, and commonly used methods include K-means (Xiao et al, 2022) and Principal Component Analysis (Veeramsetty et al, 2022). And reinforcement learning (Park et al, 2020) is different from the first two.…”

Section: Intelligent Forecasting Methods Based On Machine Learningmentioning

confidence: 99%

Review of multiple load forecasting method for integrated energy system

Liu,

Li,

et al. 2023

Front. Energy Res.

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In order to further improve the efficiency of energy utilization, Integrated Energy Systems (IES) connect various energy systems closer, which has become an important energy utilization mode in the process of energy transition. Because the complex and variable multiple load is an important part of the new power system, the load forecasting is of great significance for the planning, operation, control, and dispatching of the new power system. In order to timely track the latest research progress of the load forecasting method and grasp the current research hotspot and the direction of load forecasting, this paper reviews the relevant research content of the forecasting methods. Firstly, a brief overview of Integrated Energy Systems and load forecasting is provided. Secondly, traditional forecasting methods based on statistical analysis and intelligent forecasting methods based on machine learning are discussed in two directions to analyze the advantages, disadvantages, and applicability of different methods. Then, the results of Integrated Energy Systemss multiple load forecasting for the past 5 years are compiled and analyzed. Finally, the Integrated Energy Systems load forecasting is summarized and looked forward.

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Section: Intelligent Forecasting Methods Based On Machine Learningmentioning

confidence: 99%

Review of multiple load forecasting method for integrated energy system

Liu,

Li,

et al. 2023

Front. Energy Res.

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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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“…As mentioned in Section 1, in general the input candidates can be divided into three major groups: electric load variables, temperature variables and climate variables. A large variety of these variables are reported in the literature, including their daily means and lagged hourly values up to 3 weeks back [35].…”

Section: Problem Statement-data Preprocessingmentioning

confidence: 99%

“…It can be seen that the evening and night parts of the time-series regain their "normal" nature, and the forecaster performs accurate predictions accordingly. The suggested neurofuzzy recurrent model is now compared with three Computational Intelligence-based models reported in the literature, namely, ANFIS [16], LSTM forecaster [30,35] and the DFNN neurofuzzy model [48]. ANFIS is the most well-known neurofuzzy system, LSTM is a popular and effective Deep Learning model, while DFNN constitutes a system that shares the same underlying philosophy with ReNFuzz-LF but is more complex.…”

mentioning

confidence: 99%

ReNFuzz-LF: A Recurrent Neurofuzzy System for Short-Term Load Forecasting

2022

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A neurofuzzy system is proposed for short-term electric load forecasting. The fuzzy rule base of ReNFuzz-LF consists of rules with dynamic consequent parts that are small-scale recurrent neural networks with one hidden layer, whose neurons have local output feedback. The particular representation maintains the local learning nature of the typical static fuzzy model, since the dynamic consequent parts of the fuzzy rules can be considered as subsystems operating at the subspaces defined by the fuzzy premise parts, and they are interconnected through the defuzzification part. The Greek power system is examined, and hourly based predictions are extracted for the whole year. The recurrent nature of the forecaster leads to the use of a minimal set of inputs, since the temporal relations of the electric load time-series are identified without any prior knowledge of the appropriate past load values being necessary. An extensive simulation analysis is conducted, and the forecaster’s performance is evaluated using appropriate metrics (APE, RMSE, forecast error duration curve). ReNFuzz-LF performs efficiently, attaining an average percentage error of 1.35% and an average yearly absolute error of 86.3 MW. Finally, the performance of the proposed forecaster is compared to a series of Computational Intelligence based models, such that the learning characteristics of ReNFuzz-LF are highlighted.

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Short Term Electric Power Load Forecasting Using Principal Component Analysis and Recurrent Neural Networks

Cited by 27 publications

References 34 publications

Review of multiple load forecasting method for integrated energy system

Review of multiple load forecasting method for integrated energy system

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

ReNFuzz-LF: A Recurrent Neurofuzzy System for Short-Term Load Forecasting

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