Comparison of very short-term load forecasting techniques

Liu, K.; Subbarayan, S.; Shoults, R.R.; Manry, M.T.; Kwan, Chiman; Lewis, Frank; Naccarino, Jorge

doi:10.1109/59.496169

Cited by 263 publications

(98 citation statements)

References 13 publications

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“…Equation (4) calculates the power imbalance in this study, taking into account the HA schedule for conventional generation and power exchange (import and export power), available wind power generation and load demand, which is assumed to be equal to the HA forecast. However in real engineering, the control operator can estimate the load demand with very short term load forecasting technique [19] and system data from the SCADA/EMS. The operator after obtaining system data calculates the former forecasting error and then precisely estimates the load demand from the predicted loading in a 5 min resolution.…”

Section: Rolling Balance Controlmentioning

confidence: 99%

Real-time impact of power balancing on power system operation with large scale integration of wind power

Basit

Hansen

Sørensen

et al. 2015

J. Mod. Power Syst. Clean Energy

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Highly wind power integrated power system requires continuous active power regulation to tackle the power imbalances resulting from the wind power forecast errors. The active power balance is maintained in real-time with the automatic generation control and also from the control room, where regulating power bids are activated manually. In this article, an algorithm is developed to simulate the activation of regulating power bids, as performed in the control room, during power imbalance between generation and load demand. In addition, the active power balance is also controlled through automatic generation control, where coordinated control strategy between combined heat and power plants and wind power plant enhances the secure power system operation. The developed algorithm emulating the control room response, to deal with real-time power imbalance, is applied and investigated on the future Danish power system model. The power system model takes the hour-ahead regulating power plan from power balancing model and the generation and power exchange capacities for the year 2020 into account. The real-time impact of power balancing in a highly wind power integrated power system is assessed and discussed by means of simulations for different possible scenarios.

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Section: Rolling Balance Controlmentioning

confidence: 99%

Real-time impact of power balancing on power system operation with large scale integration of wind power

Basit

Hansen

Sørensen

et al. 2015

J. Mod. Power Syst. Clean Energy

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show abstract

“…Researchers have tried various techniques to forecast the load of the next few minutes to hours. Liu et al compared five techniques for VSTLF in [7]. Although the paper has been frequently cited, its autoregressive models were incorrectly applied to the load series.…”

Section: Introductionmentioning

confidence: 99%

Real-time anomaly detection for very short-term load forecasting

Luo

Hong

Yue

2018

J. Mod. Power Syst. Clean Energy

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Although the recent load information is critical to very short-term load forecasting (VSTLF), power companies often have difficulties in collecting the most recent load values accurately and timely for VSTLF applications. This paper tackles the problem of real-time anomaly detection in most recent load information used by VSTLF. This paper proposes a model-based anomaly detection method that consists of two components, a dynamic regression model and an adaptive anomaly threshold. The case study is developed using the data from ISO New England. This paper demonstrates that the proposed method significantly outperforms three other anomaly detection methods including two methods commonly used in the field and one state-of-the-art method used by a winning team of the Global Energy Forecasting Competition 2014. Finally, a general anomaly detection framework is proposed for the future research.

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“…Thirdly, the hybrid method is also a good solution to problems. Liu et al [20] shows that it is feasible to design a simple, satisfactory forecasting model based on fuzzy logic and neural networks to predict very short-term load trends online. Xu et al [21] developed a method for day-ahead prediction and shaping of the dynamic response of the demand at bulk supply points without field measurements, broadly based on the application of the artificial neural network, Monte Carlo simulations, and load modeling approaches.…”

Section: Introductionmentioning

confidence: 99%

An Improved Forecasting Method for Photovoltaic Power Based on Adaptive BP Neural Network with a Scrolling Time Window

Zhu

Lian

et al. 2017

Energies

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Due to the large scale of grid-connected photovoltaic (PV) power generation installations, accurate PV power forecasting is critical to the safe and economic operation of the electric power system. In the paper, by analyzing the influence of external ambient factors and the changing characteristics of PV modules with time, it is found that PV power generation is a nonlinear and time-varying process. This suggests that a certain single forecasting model is inadequate for representing actual generation characteristics, and it is difficult to obtain an accurate forecasting result. An adaptive back propagation (BP) neural network model adopting scrolling time window is proposed to solve the problem. Via an update of the training data of BP neural network with the scrolling time window, the forecasting model adapts to time and a changing external environment with the required modeling precision. Meanwhile, through evaluation of the forecasting performance in different time windows, an optimized time window can be determined to guarantee accuracy. Finally, using the actual operation data of a PV plant in Beijing, the approach is validated as being applicable for PV power forecasting and is able to effectively respond to the dynamic change of the PV power generation process. This improves the forecasting accuracy and also reduces computation complexity as compared with the conventional BP neural network algorithm.

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Comparison of very short-term load forecasting techniques

Cited by 263 publications

References 13 publications

Real-time impact of power balancing on power system operation with large scale integration of wind power

Real-time impact of power balancing on power system operation with large scale integration of wind power

Real-time anomaly detection for very short-term load forecasting

An Improved Forecasting Method for Photovoltaic Power Based on Adaptive BP Neural Network with a Scrolling Time Window

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