• 제목/요약/키워드: Short-term Load Forecasting

검색결과 108건 처리시간 0.028초

전력부하의 확률가정적 최적예상식의 유도 및 전산프로그래밍에 관한 연구 (Study on a Probabilistic Load Forecasting Formula and Its Algorithm)

  • 고명삼
    • 전기의세계
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    • 제22권2호
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    • pp.28-32
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    • 1973
  • System modeling is applied in developing a probabilistic linear estimator for the load of an electric power system for the purpose of short term load forecasting. The model assumer that the load in given by the suns of a periodic discrete time serier with a period of 24 hour and a residual term such that the output of a discrete time dynamical linear system driven by a white random process and a deterministic input. And also we have established the main forecasting algorithms, which are essemtally the Kalman filter-predictor equations.

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신경회로망을 이용한 단기부하예측 (Short-term Load Forecasting using Neural Network)

  • 고희석;이충식;김현덕;이희철
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1993년도 정기총회 및 추계학술대회 논문집 학회본부
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    • pp.29-31
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    • 1993
  • This paper presents Neural Network(NN) approach to short-term load forecasting. Input to the NN are past loads and the output is the predicted load for a given day. The NN is used to learn the relationship among past, current and future temperature and loads. Three different cases are presented. Case 1 divides into weekday and weekendday load pattern. Case 2 forcasts 24-hour ahead load. Case 3 searchs for the same load pattern as present load pattern in past load pattern. From result of forecasting, an average absolute percentage errors of case 1 shows 2.0%. That of case 2 shows 2.2, and That of case 3 shows 1.6%.

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An Approach of Dimension Reduction in k-Nearest Neighbor Based Short-term Load Forecasting

  • Chu, FaZheng;Jung, Sung-Hwan
    • 한국멀티미디어학회논문지
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    • 제20권9호
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    • pp.1567-1573
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    • 2017
  • The k-nearest neighbor (k-NN) algorithm is one of the most widely used benchmark algorithm in classification. Nowadays it has been further applied to predict time series. However, one of the main concerns of the algorithm applied on short-term electricity load forecasting is high computational burden. In the paper, we propose an approach of dimension reduction that follows the principles of highlighting the temperature effect on electricity load data series. The results show the proposed approach is able to reduce the dimension of the data around 30%. Moreover, with temperature effect highlighting, the approach will contribute to finding similar days accurately, and then raise forecasting accuracy slightly.

지원벡터머신을 이용한 단기전력 수요예측에 관한 연구 (A Study on the Short-term Load Forecasting using Support Vector Machine)

  • 조남훈;송경빈;노영수;강대승
    • 대한전기학회논문지:전력기술부문A
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    • 제55권7호
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    • pp.306-312
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    • 2006
  • Support Vector Machine(SVM), of which the foundations have been developed by Vapnik (1995), is gaining popularity thanks to many attractive features and promising empirical performance. In this paper, we propose a new short-term load forecasting technique based on SVM. We discuss the input vector selection of SVM for load forecasting and analyze the prediction performance for various SVM parameters such as kernel function, cost coefficient C, and $\varepsilon$ (the width of 8 $\varepsilon-tube$). The computer simulation shows that the prediction performance of the proposed method is superior to that of the conventional neural networks.

A New Approach to Short-term Price Forecast Strategy with an Artificial Neural Network Approach: Application to the Nord Pool

  • Kim, Mun-Kyeom
    • Journal of Electrical Engineering and Technology
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    • 제10권4호
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    • pp.1480-1491
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    • 2015
  • In new deregulated electricity market, short-term price forecasting is key information for all market players. A better forecast of market-clearing price (MCP) helps market participants to strategically set up their bidding strategies for energy markets in the short-term. This paper presents a new prediction strategy to improve the need for more accurate short-term price forecasting tool at spot market using an artificial neural networks (ANNs). To build the forecasting ANN model, a three-layered feedforward neural network trained by the improved Levenberg-marquardt (LM) algorithm is used to forecast the locational marginal prices (LMPs). To accurately predict LMPs, actual power generation and load are considered as the input sets, and then the difference is used to predict price differences in the spot market. The proposed ANN model generalizes the relationship between the LMP in each area and the unconstrained MCP during the same period of time. The LMP calculation is iterated so that the capacity between the areas is maximized and the mechanism itself helps to relieve grid congestion. The addition of flow between the areas gives the LMPs a new equilibrium point, which is balanced when taking the transfer capacity into account, LMP forecasting is then possible. The proposed forecasting strategy is tested on the spot market of the Nord Pool. The validity, the efficiency, and effectiveness of the proposed approach are shown by comparing with time-series models

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

  • 김기수;류구현;송경빈
    • 전기학회논문지
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    • 제58권9호
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    • pp.1695-1699
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    • 2009
  • This paper analyzed the characteristics of the demand of electric power in Jeju by year, day. For this analysis, this research used the correlation between the changes in the temperature and the demand of electric power in summer, and cleaned the data of the characteristics of the temperatures, using the coefficient of correlation as the standard. And it proposed the algorithm of forecasting the short-term electric power demand in Jeju, Therefore, in the case of summer, the data by each cleaned temperature section were used. Based on the data, this paper forecasted the short-term electric power demand in the exponential smoothing method. Through the forecast of the electric power demand, this paper verified the excellence of the proposed technique by comparing with the monthly report of Jeju power system operation result made by Korea Power Exchange-Jeju.

전력부하 유형에 따른 신경회로망 단기부하예측에 관한 연구 (Short-term Load Forecasting Using Neural Networks By Electrical Load Pattern)

  • 박후식;이상성;김형수;문경준;박준호
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1997년도 하계학술대회 논문집 D
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    • pp.914-916
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    • 1997
  • This paper presents the development of an Artificial Neural Networks(ANN) for Short-Term Load Forecasting(STLF). First, used historical load data is divided into 5 patterns for the each seasonal data using Kohonen networks. Second, classified data is used as inputs of Back-propagation networks for next day hourly load forecasting. The proposed method was tested with KEPCO hourly record (1994-95) and we obtained desirable results.

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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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    • 제9권4호
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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.

실시간기상정보와 전력패턴을 이용한 단기 전력부하예측 (Short-term Electric Load Forecasting Using the Realtime Weather Information & Electric Power Pattern Analysis)

  • 김일주;이송근
    • 전기학회논문지
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    • 제65권6호
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    • pp.934-939
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    • 2016
  • This paper made short-term electric load forecasting by using temperature data at three-hour intervals (9am, 12pm, 3pm, and 6pm) provided by the Korea Meteorological Administration (KMA). In addition, the electric power pattern was created using existing electric power data, and temperature sensitivity was derived using temperature and electric power data. We made power load forecasting program using LabVIEW, a graphic language.

코호넨 신경망을 이용한 단기 전력수요 예측 (Short Term Load Forecasting Using The Kohonen Neural Network)

  • 조승우;황갑주
    • 대한전기학회:학술대회논문집
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    • 대한전기학회 1996년도 추계학술대회 논문집 학회본부
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    • pp.447-449
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    • 1996
  • This paper describes an algorithm for short term load forecasting using the Kohonen neural network. Single layer Kohonen neural network presents a lot of advantageous features for practical application. It takes less training time compared to other networks such as BP network, and moreover, its self organized feature can amend the distorted data. The originality of proposed approach is to use a Kohonen map toclassify data representing load patterns and to use directly the information stored in the weight vectors of the Kohonen map to pridict the load. Proposed method was tested with KEPCO hourly record(1993-1995) show better forecasting results compared with conventional exponential smoothing method.

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