An evaluation of methods for very short-term load forecasting using minute-by-minute British data

Taylor, James W.

doi:10.1016/j.ijforecast.2008.07.007

Cited by 204 publications

(125 citation statements)

References 25 publications

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“…Taylor [2] used minute-by-minute British electricity load data to predict the load 10-30 minutes ahead. He studied a number of statistical methods based on Autoregressive Integrated Moving Average (ARIMA) and exponential smoothing.…”

Section: Previous Work On Vstlfmentioning

confidence: 99%

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Electricity load forecasting based on autocorrelation analysis

Sood

Koprinska

Agelidis

2010

The 2010 International Joint Conference on Neural Networks (IJCNN)

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Abstract-We present new approaches for 5-minute ahead electricity load forecasting. They were evaluated on data from the Australian electricity market operator for 2006-2008. After examining the load characteristics using autocorrelation analysis with 4-week sliding window, we selected 51 features. Using this feature set with linear regression and support vector regression we achieved an improvement of 7.56% in the Mean Absolute Percentage Error (MAPE) over the industry model which uses backpropagation neural network. We then investigated the application of a number of methods for further feature subset selection. Using a subset of 38 and 14 of these features with the same algorithms we were able to achieve an improvement of 6.53% and 4.81% in MAPE, respectively, over the industry model.

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Section: Previous Work On Vstlfmentioning

confidence: 99%

“…of abnormal peak [2]. Finally, forecasting errors have significant implications for profits, market shares and shareholder values [3].…”

mentioning

confidence: 99%

Electricity load forecasting based on autocorrelation analysis

Sood

Koprinska

Agelidis

2010

The 2010 International Joint Conference on Neural Networks (IJCNN)

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“…Charytoniuk and Chen proposed another approach using a set of ANNs to model the load dynamics instead of the actual loads [8]. For VSTLF, Taylor used the observations of minute-by-minute British electricity demand to evaluate various methods including autoregressive integrated moving average (ARIMA) models and two exponential smoothing methods [9]. Alamaniotis et al proposed an ensemble of kernel-based Gaussian processes [10].…”

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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“…In general, these early studies can be classified into two major categories: econometric [2][3][4][5][6][7][8][9] and machine learning (ML) methods [10][11][12][13][14][15][16][17][18][19][20][21][22][23]. The artificial intelligence (AI) energy forecasting model, which is a class of ML method, has gained popularity in recent years because of its superiority in time series processing and its capability to deal with noise data.…”

Section: Introductionmentioning

confidence: 99%

Energy Demand Forecasting: Combining Cointegration Analysis and Artificial Intelligence Algorithm

Huang

Tang

Chen

2018

Mathematical Problems in Engineering

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Energy is vital for the sustainable development of China. Accurate forecasts of annual energy demand are essential to schedule energy supply and provide valuable suggestions for developing related industries. In the existing literature on energy use prediction, the artificial intelligence-based (AI-based) model has received considerable attention. However, few econometric and statistical evidences exist that can prove the reliability of the current AI-based model, an area that still needs to be addressed. In this study, a new energy demand forecasting framework is presented at first. On the basis of historical annual data of electricity usage over the period of 1985-2015, the coefficients of linear and quadratic forms of the AI-based model are optimized by combining an adaptive genetic algorithm and a cointegration analysis shown as an example. Prediction results of the proposed model indicate that the annual growth rate of electricity demand in China will slow down. However, China will continue to demand about 13 trillion kilowatt hours in 2030 because of population growth, economic growth, and urbanization. In addition, the model has greater accuracy and reliability compared with other single optimization methods.

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An evaluation of methods for very short-term load forecasting using minute-by-minute British data

Cited by 204 publications

References 25 publications

Electricity load forecasting based on autocorrelation analysis

Electricity load forecasting based on autocorrelation analysis

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

Energy Demand Forecasting: Combining Cointegration Analysis and Artificial Intelligence Algorithm

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