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  • Title/Summary/Keyword: Power Load Forecasting

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Temporal Classification Method for Forecasting Power Load Patterns From AMR Data

  • Lee, Heon-Gyu;Shin, Jin-Ho;Park, Hong-Kyu;Kim, Young-Il;Lee, Bong-Jae;Ryu, Keun-Ho
    • Korean Journal of Remote Sensing
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    • v.23 no.5
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    • pp.393-400
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    • 2007
  • We present in this paper a novel power load prediction method using temporal pattern mining from AMR(Automatic Meter Reading) data. Since the power load patterns have time-varying characteristic and very different patterns according to the hour, time, day and week and so on, it gives rise to the uninformative results if only traditional data mining is used. Also, research on data mining for analyzing electric load patterns focused on cluster analysis and classification methods. However despite the usefulness of rules that include temporal dimension and the fact that the AMR data has temporal attribute, the above methods were limited in static pattern extraction and did not consider temporal attributes. Therefore, we propose a new classification method for predicting power load patterns. The main tasks include clustering method and temporal classification method. Cluster analysis is used to create load pattern classes and the representative load profiles for each class. Next, the classification method uses representative load profiles to build a classifier able to assign different load patterns to the existing classes. The proposed classification method is the Calendar-based temporal mining and it discovers electric load patterns in multiple time granularities. Lastly, we show that the proposed method used AMR data and discovered more interest patterns.

Planning for Construction and Expanding of Distribution Substation Considering Contingency (상정사고를 고려한 배전용 변전소 신,증설 계획 수립)

  • Choi, Sang-Bong;Kim, Dae-Kyeong;Jeong, Seong-Hwan
    • The Transactions of the Korean Institute of Electrical Engineers A
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    • v.50 no.7
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    • pp.303-308
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    • 2001
  • This paper presents algorithm to plan construction and expanding of substation considering contingency accidents by proposing utilization factor according to configuration of substation bank system. In this paper, firstly, proper sphere of supply area by each district which could be standardized with respect to its supply capacity is established under assumption of long term load forecasting. Secondly, goal of utilization ratio based on configuration of substation bank was set to keep reliability by remaining sound bank when it happen to one bank accidents. Finally, it is set up for optimal construction and expanding of substation considering economy and reliability simultaneously about substation to exceed these ratio. To verify proposed algorithm, at first, after adopting a part of Kangnam area in Seoul as area for testing, it is divided into several regions for this area according to power branches of power utility. Secondly, by deriving correlation factor between load demand and economic indicators in these region respectively, the regional load forecasting was performed with economic growth and city plan scenario. Finally, based on the predicted load demand by region and land use data which is identified from air-photographic, the load demand by district was predicted. Also, planning for substation considering contingency is formulated to expand taking into account computing utilization factor which is based on configuration of substation bank respectively.

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A study on the Electrical Load Pattern Classification and Forecasting using Neural Network (신경회로망을 이용한 전력부하의 유형분류 및 예측에 관한 연구)

  • Park, June-Ho;Shin, Gil-Jae;Lee, Hwa-Suk
    • Proceedings of the KIEE Conference
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    • 1991.11a
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    • pp.39-42
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    • 1991
  • The Application of Artificial Neural Network(ANN) to forecast a load in a power system is investigated. The load forecasting is important in the electric utility industry. This technique, methodology based on the fact that parallel structure can process very fast much information is a promising approach to a load forecasting. ANN that is highly interconnected processing element in a hierachy activated by the each input. The load pattern can be divided distinctively into two patterns, that is, weekday and weekend. ANN is composed of a input layer, several hidden layers, and a output layer and the past data is used to activate input layer. The output of ANN is the load forecast for a given day. The result of this simulation can be used as a reference to a electric utility operation.

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Short-term Load Forecasting of Using Data refine for Temperature Characteristics at Jeju Island (온도특성에 대한 데이터 정제를 이용한 제주도의 단기 전력수요 예측)

  • Kim, Ki-Su;Song, Kyung-Bin
    • Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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    • 2008.10a
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    • pp.225-228
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    • 2008
  • The electricity supply and demand to be stable to a system link increase of the variance power supply and operation are requested in jeju Island electricity system. A short-term Load forecasting which uses the characteristic of the Load is essential consequently. We use the interrelationship of the electricity Load and change of a summertime temperature and data refining in the paper. We presented a short-term Load forecasting algorithm of jeju Island and used the correlation coefficient to the criteria of the refining. We used each temperature area data to be refined and forecasted a short-term Load to an exponential smoothing method.

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Daily Peak Electric Load Forecasting Using Neural Network and Fuzzy System (신경망과 퍼지시스템을 이용한 일별 최대전력부하 예측)

  • Bang, Young-Keun;Kim, Jae-Hyoun;Lee, Chul-Heui
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.67 no.1
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    • pp.96-102
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    • 2018
  • For efficient operating strategy of electric power system, forecasting of daily peak electric load is an important but difficult problem. Therefore a daily peak electric load forecasting system using a neural network and fuzzy system is presented in this paper. First, original peak load data is interpolated in order to overcome the shortage of data for effective prediction. Next, the prediction of peak load using these interpolated data as input is performed in parallel by a neural network predictor and a fuzzy predictor. The neural network predictor shows better performance at drastic change of peak load, while the fuzzy predictor yields better prediction results in gradual changes. Finally, the superior one of two predictors is selected by the rules based on rough sets at every prediction time. To verify the effectiveness of the proposed method, the computer simulation is performed on peak load data in 2015 provided by KPX.

Deep Neural Network Model For Short-term Electric Peak Load Forecasting (단기 전력 부하 첨두치 예측을 위한 심층 신경회로망 모델)

  • Hwang, Heesoo
    • Journal of the Korea Convergence Society
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    • v.9 no.5
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    • pp.1-6
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    • 2018
  • In smart grid an accurate load forecasting is crucial in planning resources, which aids in improving its operation efficiency and reducing the dynamic uncertainties of energy systems. Research in this area has included the use of shallow neural networks and other machine learning techniques to solve this problem. Recent researches in the field of computer vision and speech recognition, have shown great promise for Deep Neural Networks (DNN). To improve the performance of daily electric peak load forecasting the paper presents a new deep neural network model which has the architecture of two multi-layer neural networks being serially connected. The proposed network model is progressively pre-learned layer by layer ahead of learning the whole network. For both one day and two day ahead peak load forecasting the proposed models are trained and tested using four years of hourly load data obtained from the Korea Power Exchange (KPX).

A Study on the Electric System Design by the Forecasting of Maximum Demand (최대수요전력 예측에 의한 전기계통 설계에 관한 연구)

  • 황규태;김수석
    • The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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    • v.6 no.1
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    • pp.29-39
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    • 1992
  • In this paper, the basic idea of optimum electric system design by means of the forecasting of maximum demand is presented, and the load characteristics and practical operating conditions are based on the technical data. After reconstruction of th model plant by use of above method, power supply reliability, future extention, initial cost, and running cost saving effects are analyzed. As a result, it is verified that the systems wherein the power is supply to each load frm main transformer whose capacity is calculated by forecasting are economic rather than the systems wherein the power is supply to each electric feeders from each corresponding transformer.

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A Study on Supplied Forecasting of Short-term Electrical Power using Fuzzy Compensative Algorithm

  • Choo Yeon-Gyu;Lee Kwang-Seok;Kim Hyun-Duck
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2006.05a
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    • pp.779-783
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    • 2006
  • A The estimation of electrical power consumption is becoming more important to supply stabilized electrical power recently. In this paper, we propose a supplied forecasting system of electrical power using Fuzzy Compensative Algorithm to estimate electrical load accurately than the previous. We evaluate a time series of supplied electrical power have the chaotic character using quantitative and qualitative analysis, compose a forecasting system by the maximum change rate(α) of Fuzzy Algorithm and compensative parameter. Simulating it for obtained time series, we can obtain more accurate results than the previous proposed system.

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Short-Term Load Forecast in Microgrids using Artificial Neural Networks (신경회로망을 이용한 마이크로그리드 단기 전력부하 예측)

  • Chung, Dae-Won;Yang, Seung-Hak;You, Yong-Min;Yoon, Keun-Young
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.66 no.4
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    • pp.621-628
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    • 2017
  • This paper presents an artificial neural network (ANN) based model with a back-propagation algorithm for short-term load forecasting in microgrid power systems. Owing to the significant weather factors for such purpose, relevant input variables were selected in order to improve the forecasting accuracy. As remarked above, forecasting is more complex in a microgrid because of the increased variability of disaggregated load curves. Accurate forecasting in a microgrid will depend on the variables employed and the way they are presented to the ANN. This study also shows numerically that there is a close relationship between forecast errors and the number of training patterns used, and so it is necessary to carefully select the training data to be employed with the system. Finally, this work demonstrates that the concept of load forecasting and the ANN tools employed are also applicable to the microgrid domain with very good results, showing that small errors of Mean Absolute Percentage Error (MAPE) around 3% are achievable.

Real-Time Peak Shaving Algorithm Using Fuzzy Wind Power Generation Curves for Large-Scale Battery Energy Storage Systems

  • Son, Subin;Song, Hwachang
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.14 no.4
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    • pp.305-312
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    • 2014
  • This paper discusses real-time peak shaving algorithms for a large-scale battery energy storage system (BESS). Although several transmission and distribution functions could be implemented for diverse purposes in BESS applications, this paper focuses on a real-time peak shaving algorithm for an energy time shift, considering wind power generation. In a high wind penetration environment, the effective load levels obtained by subtracting the wind generation from the load time series at each long-term cycle time unit are needed for efficient peak shaving. However, errors can exist in the forecast load and wind generation levels, and the real-time peak shaving operation might require a method for wind generation that includes comparatively large forecasting errors. To effectively deal with the errors of wind generation forecasting, this paper proposes a real-time peak shaving algorithm for threshold value-based peak shaving that considers fuzzy wind power generation.