On-Line Algorithms for Forecasting Hourly Loads of an Electric Utility

Vermuri,; Huang, Adam; nelson,

doi:10.1109/mper.1981.5511769

Cited by 22 publications

(2 citation statements)

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“…In the literature, there exist many time series forecasting models, e.g., autoregressive (AR) models, moving average (MA) models, autoregressive moving average (ARMA) models [17]- [19], autoregressive integrated moving average (ARIMA) models [20], and ARMA model with exogenous inputs (ARMAX) models. The comparisons among different time series models can be found in [21]- [23]. Load forecasting can also be accomplished by adopting artificial neural network [24], [25] and Kalman filter [23], [26], [27].…”

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confidence: 99%

Load Forecasting and Operation Strategy Design for CCHP Systems Using Forecasted Loads

Liu

Shi

Fang

2015

IEEE Trans. Contr. Syst. Technol.

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Operation strategy of combined cooling, heating, and power (CCHP) systems is designed to collect users' load information to determine the energy input to the system and power flow inside the system. Most of the current operation strategies are designed by assuming that accurate loads during the next time interval are already known. To solve the problem of unknown loads in practical applications, using an autoregressive moving average with exogenous inputs model, whose parameters are identified by an ordinary least squares-two-stage recursive least squares (TSRLS) algorithm, cooling, heating, and electrical loads in the future time intervals are forecasted. The identification procedure uses the dew-point temperature as the instrumental variable for the exogenous variable (dry-bulb temperature), to better characterize the relationship between exogenous and endogenous variables. TSRLS helps to reduce the space and time complexity. A poststrategy is also proposed to compensate for the inaccurate forecasting. A case study is conducted to verify the feasibility and effectiveness of the proposed methods. IndexTerms-Autoregressive moving average with exogenous (ARMAX) input, combined cooling, heating, and power (CCHP), load forecasting, optimal operation strategy, poststrategy, two-stage recursive least squares (TSRLS).

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confidence: 99%

Load Forecasting and Operation Strategy Design for CCHP Systems Using Forecasted Loads

Liu

Shi

Fang

2015

IEEE Trans. Contr. Syst. Technol.

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“…Various forecasting techniques have been proposed in the last few decades. Those models include: Time Series [1], [2], Multiple Linear Regression [3], Auto Regressive Moving Average (ARMA) [4]. The time series model uses the historical load data for extrapolation of future loads.…”

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confidence: 99%

Integrated genetic algorithm/tabu search and neural fuzzy networks for short-term load forecasting

Liao

Tsao

IEEE Power Engineering Society General Meeting, 2004.

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An Integrated Genetic Algorithm (GA) /Fuzzy System (FS), Tabu Search (TS) and Neural Fuzzy Network (NFN) method for load forecasting is presented in this paper.A Fuzzy Hyper-Rectangular Composite Neural Networks (FHRCNNs) was used for the initial load forecasting. Then we used CGAFS and TS to find the optimal solution of the parameters of the FHRCNNs, instead of Back-Propagation (BP)( including parameters such as synaptic weights, biases, membership functions, sensitivity factor in membership functions and adjustable synaptic weights). First the CGAFS generates a set of feasible solution parameters and then puts the solution into the TS. The CGAFS has good global optimal search capabilities, but poor local optimal search capabilities. The TS method on the other hand has good local optimal search capabilities. We combined both methods to try and obtain both advantages, and in doing so eliminate the drawback of the traditional ANN training by BP. Finally, we used the (GAFSTS-NFN) to see if we could improve the quality of the solution, and if we actually could reduce the error of load forecasting. The proposed CGAFS-TS load forecasting scheme was tested using data obtain from a sample study, including one year, one week and 24-hours time periods. The results demonstrated the accuracy of the proposed load forecasting scheme.

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A study of two on-line prediction algorithms

Mahalanabis

Hanmandlu²

1984

Computers & Electrical Engineering

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On-Line Algorithms for Forecasting Hourly Loads of an Electric Utility

Cited by 22 publications

References 0 publications

Load Forecasting and Operation Strategy Design for CCHP Systems Using Forecasted Loads

Load Forecasting and Operation Strategy Design for CCHP Systems Using Forecasted Loads

Integrated genetic algorithm/tabu search and neural fuzzy networks for short-term load forecasting

A study of two on-line prediction algorithms

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