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  • Title/Summary/Keyword: Power Load Forecasting

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Locally-Weighted Polynomial Neural Network for Daily Short-Term Peak Load Forecasting

  • Yu, Jungwon;Kim, Sungshin
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.16 no.3
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    • pp.163-172
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    • 2016
  • Electric load forecasting is essential for effective power system planning and operation. Complex and nonlinear relationships exist between the electric loads and their exogenous factors. In addition, time-series load data has non-stationary characteristics, such as trend, seasonality and anomalous day effects, making it difficult to predict the future loads. This paper proposes a locally-weighted polynomial neural network (LWPNN), which is a combination of a polynomial neural network (PNN) and locally-weighted regression (LWR) for daily shortterm peak load forecasting. Model over-fitting problems can be prevented effectively because PNN has an automatic structure identification mechanism for nonlinear system modeling. LWR applied to optimize the regression coefficients of LWPNN only uses the locally-weighted learning data points located in the neighborhood of the current query point instead of using all data points. LWPNN is very effective and suitable for predicting an electric load series with nonlinear and non-stationary characteristics. To confirm the effectiveness, the proposed LWPNN, standard PNN, support vector regression and artificial neural network are applied to a real world daily peak load dataset in Korea. The proposed LWPNN shows significantly good prediction accuracy compared to the other methods.

Short-term Electrical Load Forecasting Using Neuro-Fuzzy Model with Error Compensation

  • Wang, Bo-Hyeun
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.9 no.4
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    • pp.327-332
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    • 2009
  • This paper proposes a method to improve the accuracy of a short-term electrical load forecasting (STLF) system based on neuro-fuzzy models. The proposed method compensates load forecasts based on the error obtained during the previous prediction. The basic idea behind this approach is that the error of the current prediction is highly correlated with that of the previous prediction. This simple compensation scheme using error information drastically improves the performance of the STLF based on neuro-fuzzy models. The viability of the proposed method is demonstrated through the simulation studies performed on the load data collected by Korea Electric Power Corporation (KEPCO) in 1996 and 1997.

Development of An Yearly Load Forecasting System (연간수요예측시스템의 개발)

  • Choo, Jin-Boo;Lee, Cheol-Hyu;Jeon, Dong-Hun;Kim, Sung-Hak;Hwang, Kab-Ju
    • Proceedings of the KIEE Conference
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    • 1996.07b
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    • pp.908-912
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    • 1996
  • The yearly load forecasting system has been developed for the economic and secure operation of electric power system. It forecasts yearly peak load and thereafter deduces hourly load using the top-down approach. Relative coefficient model has been applied to estimate peak load of a specific date or a specific day of the week. It is equipped with graphic user interface which enables a user to easily access to the system. Yearly average forecasting error may be reduced to 23(%) only if we can forecast summer-time temperature correctly.

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Large Language Models-based Feature Extraction for Short-Term Load Forecasting (거대언어모델 기반 특징 추출을 이용한 단기 전력 수요량 예측 기법)

  • Jaeseung Lee;Jehyeok Rew
    • Journal of Korea Society of Industrial Information Systems
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    • v.29 no.3
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    • pp.51-65
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    • 2024
  • Accurate electrical load forecasting is important to the effective operation of power systems in smart grids. With the recent development in machine learning, artificial intelligence-based models for predicting power demand are being actively researched. However, since existing models get input variables as numerical features, the accuracy of the forecasting model may decrease because they do not reflect the semantic relationship between these features. In this paper, we propose a scheme for short-term load forecasting by using features extracted through the large language models for input data. We firstly convert input variables into a sentence-like prompt format. Then, we use the large language model with frozen weights to derive the embedding vectors that represent the features of the prompt. These vectors are used to train the forecasting model. Experimental results show that the proposed scheme outperformed models based on numerical data, and by visualizing the attention weights in the large language models on the prompts, we identified the information that significantly influences predictions.

Short-term Power Load Forecasting using Time Pattern for u-City Application (u-City응용에서의 시간 패턴을 이용한 단기 전력 부하 예측)

  • Park, Seong-Seung;Shon, Ho-Sun;Lee, Dong-Gyu;Ji, Eun-Mi;Kim, Hi-Seok;Ryu, Keun-Ho
    • Journal of Korea Spatial Information System Society
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    • v.11 no.2
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    • pp.177-181
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    • 2009
  • Developing u-Public facilities for application u-City is to combine both the state-of-the art of the construction and ubiquitous computing and must be flexibly comprised of the facilities for the basic service of the building such as air conditioning, heating, lighting and electric equipments to materialize a new format of spatial planning and the public facilities inside or outside. Accordingly, in this paper we suggested the time pattern system for predicting the most basic power system loads for the basic service. To application the tim e pattern we applied SOM algorithm and k-means method and then clustered the data each weekday and each time respectively. The performance evaluation results of suggestion system showed that the forecasting system better the ARIMA model than the exponential smoothing method. It has been assumed that the plan for power supply depending on demand and system operation could be performed efficiently by means of using such power load forecasting.

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Deep Learning Based Short-Term Electric Load Forecasting Models using One-Hot Encoding (원-핫 인코딩을 이용한 딥러닝 단기 전력수요 예측모델)

  • Kim, Kwang Ho;Chang, Byunghoon;Choi, Hwang Kyu
    • Journal of IKEEE
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    • v.23 no.3
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    • pp.852-857
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    • 2019
  • In order to manage the demand resources of project participants and to provide appropriate strategies in the virtual power plant's power trading platform for consumers or operators who want to participate in the distributed resource collective trading market, it is very important to forecast the next day's demand of individual participants and the overall system's electricity demand. This paper developed a power demand forecasting model for the next day. For the model, we used LSTM algorithm of deep learning technique in consideration of time series characteristics of power demand forecasting data, and new scheme is applied by applying one-hot encoding method to input/output values such as power demand. In the performance evaluation for comparing the general DNN with our LSTM forecasting model, both model showed 4.50 and 1.89 of root mean square error, respectively, and our LSTM model showed high prediction accuracy.

A Novel Second Order Radial Basis Function Neural Network Technique for Enhanced Load Forecasting of Photovoltaic Power Systems

  • Farhat, Arwa Ben;Chandel, Shyam.Singh;Woo, Wai Lok;Adnene, Cherif
    • International Journal of Computer Science & Network Security
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    • v.21 no.2
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    • pp.77-87
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    • 2021
  • In this study, a novel improved second order Radial Basis Function Neural Network based method with excellent scheduling capabilities is used for the dynamic prediction of short and long-term energy required applications. The effectiveness and the reliability of the algorithm are evaluated using training operations with New England-ISO database. The dynamic prediction algorithm is implemented in Matlab and the computation of mean absolute error and mean absolute percent error, and training time for the forecasted load, are determined. The results show the impact of temperature and other input parameters on the accuracy of solar Photovoltaic load forecasting. The mean absolute percent error is found to be between 1% to 3% and the training time is evaluated from 3s to 10s. The results are also compared with the previous studies, which show that this new method predicts short and long-term load better than sigmoidal neural network and bagged regression trees. The forecasted energy is found to be the nearest to the correct values as given by England ISO database, which shows that the method can be used reliably for short and long-term load forecasting of any electrical system.

Seasonal load forecasting algorithm using wavelet transform analysis (웨이브릿 변환을 이용한 계절별 부하예측 알고리즘)

  • Kim, Chang-Il;Kim, Bong-Tae;Kim, Woo-Hyun;Yu, In-Keun
    • Proceedings of the KIEE Conference
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    • 1999.11b
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    • pp.242-244
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    • 1999
  • This paper proposes a novel wavelet transform based algorithm for the seasonal load forecasting. In this paper, Daubechies DB2, DB4 and DB10 wavelet transforms are adopted to predict the seasonal loads and the numerical results reveal that certain wavelet components can effectively be used to identify the load characteristics in electric power systems. The wavelet coefficients associated with certain frequency and time localization are adjusted using the conventional multiple regression method and then reconstructed. In order to forecast the final loads through a four-scale synthesis technique. The outcome of the study clearly indicates that the wavelet transform approach can be used as an attractive and effective means of the seasonal load forecasting.

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Daily Peak Load Forecasting for Electricity Demand by Time series Models (시계열 모형을 이용한 일별 최대 전력 수요 예측 연구)

  • Lee, Jeong-Soon;Sohn, H.G.;Kim, S.
    • The Korean Journal of Applied Statistics
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    • v.26 no.2
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    • pp.349-360
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    • 2013
  • Forecasting the daily peak load for electricity demand is an important issue for future power plants and power management. We first introduce several time series models to predict the peak load for electricity demand and then compare the performance of models under the RMSE(root mean squared error) and MAPE(mean absolute percentage error) criteria.

Load Forecasting Of Power System (전력계통의 전력수요예측)

  • Ahn, Dae-Hoon;Lee, Sang-Joong
    • Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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    • 2005.05a
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    • pp.78-83
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    • 2005
  • This article suggests an improved method for more accurate load forecast for the power system. The authors propose an improved load forecast expert system based on expert's know-how. A field manual for the load forecast can be made using proposed method. The authors expect this article could give a guidance to those who wish to be load forecast expert.

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