Probabilistic electric load forecasting: A tutorial review

Hong, Tao; Fan, Shangchun

doi:10.1016/j.ijforecast.2015.11.011

Cited by 973 publications

(605 citation statements)

References 83 publications

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“…On the other hand, there is an argument that it is the data and the application of interest that determine the proper methodology for each case, rather than vice versa (Hong and Fan 2016). Another argument is that perhaps research should invest more on probabilistic forecasting (e.g.…”

Section: Time Series Forecasting In Hydrology and Beyondmentioning

confidence: 99%

“…As commented in Hong and Fan (2016) the number of original techniques is countable and exhausted, therefore researchers combine them (the so-called hybrid techniques) to introduce "new" techniques. Most of them (and the accompanying papers) are useless, however researchers test them in manipulated datasets to ensure publication and introduce "superior alternatives", "powerful tools"…”

Section: Contribution In Hydrology and Beyondmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of Stochastic and Machine Learning Methods for Multi-Step Ahead Forecasting of Hydrological Processes

Papacharalampous¹,

Tyralis²,

Koutsoyiannis³

2017

Preprint

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Abstract:We perform an extensive comparison between 11 stochastic to 9 machine learning methods regarding their multi-step ahead forecasting properties by conducting 12 large-scale computational experiments. Each of these experiments uses 2 000 time series generated by linear stationary stochastic processes. We conduct each simulation experiment twice; the first time using time series of 110 values and the second time using time series of 310 values. Additionally, we conduct 92 real-world case studies using mean monthly time series of streamflow and particularly focus on one of them to reinforce the findings and highlight important facts. We quantify the performance of the methods using 18 metrics. The results indicate that the machine learning methods do not differ dramatically from the stochastic, while none of the methods under comparison is uniformly better or worse than the rest. However, there are methods that are regularly better or worse than others according to specific metrics.

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Section: Time Series Forecasting In Hydrology and Beyondmentioning

confidence: 99%

Section: Contribution In Hydrology and Beyondmentioning

confidence: 99%

Comparison of Stochastic and Machine Learning Methods for Multi-Step Ahead Forecasting of Hydrological Processes

Papacharalampous¹,

Tyralis²,

Koutsoyiannis³

2017

Preprint

View full text Add to dashboard Cite

show abstract

“…STLF has been extensively studied over the past several decades, as summarized by several review articles [1][2][3][4]. A recent development on STLF was through the Global Energy Forecasting Competition 2012 (GEFCom2012) [5].…”

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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“…Electricity load forecasting can be classified into long-term [3], medium-term [4], short-term [5][6][7] and ultra-short term [8], and the cut-off points for these four categories are three years, two weeks, and one day, respectively [9]. The short-term load forecasting (STLF), which is applied to horizons no more than one day ahead, can result in significant environmental and economic benefits for energy systems.…”

Section: Introductionmentioning

confidence: 99%

GMDH-Based Semi-Supervised Feature Selection for Electricity Load Classification Forecasting

2018

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With the development of smart power grids, communication network technology and sensor technology, there has been an exponential growth in complex electricity load data. Irregular electricity load fluctuations caused by the weather and holiday factors disrupt the daily operation of the power companies. To deal with these challenges, this paper investigates a day-ahead electricity peak load interval forecasting problem. It transforms the conventional continuous forecasting problem into a novel interval forecasting problem, and then further converts the interval forecasting problem into the classification forecasting problem. In addition, an indicator system influencing the electricity load is established from three dimensions, namely the load series, calendar data, and weather data. A semi-supervised feature selection algorithm is proposed to address an electricity load classification forecasting issue based on the group method of data handling (GMDH) technology. The proposed algorithm consists of three main stages: (1) training the basic classifier; (2) selectively marking the most suitable samples from the unclassified label data, and adding them to an initial training set; and (3) training the classification models on the final training set and classifying the test samples. An empirical analysis of electricity load dataset from four Chinese cities is conducted. Results show that the proposed model can address the electricity load classification forecasting problem more efficiently and effectively than the FW-Semi FS (forward semi-supervised feature selection) and GMDH-U (GMDH-based semi-supervised feature selection for customer classification) models.

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Probabilistic electric load forecasting: A tutorial review

Cited by 973 publications

References 83 publications

Comparison of Stochastic and Machine Learning Methods for Multi-Step Ahead Forecasting of Hydrological Processes

Comparison of Stochastic and Machine Learning Methods for Multi-Step Ahead Forecasting of Hydrological Processes

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

GMDH-Based Semi-Supervised Feature Selection for Electricity Load Classification Forecasting

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