Day-ahead short-term load probability density forecasting method with a decomposition-based quantile regression forest

He, Feifei; Zhou, Jing; Li, Mo; Feng, Kuaile; Liu, Guangbiao; He, Zhongzheng

doi:10.1016/j.apenergy.2019.114396

Cited by 88 publications

(40 citation statements)

References 36 publications

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“…In contrast to mid-and long-term forecast, which make predictions months and years prior to the event, the short-term load forecast have a shorter outlook which range from one hour to weeks before the settlement period [21,22]. Short-term load forecasting plays an important role in scheduling the power plants efficiently in electricity market, as it is essential for economic dispatch and unit commitment [23]. As a result, an improved forecast accuracy leads to a more reliable and affordable power system [21].…”

Section: Load Forecast Errormentioning

confidence: 99%

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Impact of the COVID-19 Lockdown on the Electricity System of Great Britain: A Study on Energy Demand, Generation, Pricing and Grid Stability

Kirli¹,

Parzen²,

Kiprakis³

2020

Preprint

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The outbreak of SARS-COV-2 disease 2019 (COVID-19) abruptly changed the patterns in electricity consumption, challenging the system operations of forecasting and balancing supply and demand. This is due to the mitigation measures that include lockdown and Work from Home (WFH), which decreased the aggregated demand and remarkably altered its profile. Here, we characterise these changes with various quantitative markers and compare it with pre-COVID-19 business-as-usual data using Great Britain (GB) as a case study. The ripple effects on the generation portfolio, system frequency, forecasting accuracy and imbalance pricing are also analysed. An energy data extraction and pre-processing pipeline that can be used in a variety of similar studies is also presented. Analysis of the GB demand data during the March 2020 lockdown indicates that a shift to WFH will result to a net benefit for flexible stakeholders, such as consumer on variable tariffs. Furthermore, the analysis illustrates a need for faster and more frequent balancing actions, as a result of the increased share of renewable energy in the generation mix. This new equilibrium of energy demand and supply will require a redesign of the existing balancing mechanisms as well as the longer-term power system planning strategies.

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Section: Load Forecast Errormentioning

confidence: 99%

“…This is shown in Figure 10. The forecast error is evaluated by one of the most common performance indicators, namely the mean absolute percentage error (MAPE) [21,23]. MAPE functions well as a forecast performance indicator when employing historical data.…”

Section: Load Forecast Errormentioning

confidence: 99%

Impact of the COVID-19 Lockdown on the Electricity System of Great Britain: A Study on Energy Demand, Generation, Pricing and Grid Stability

Kirli¹,

Parzen²,

Kiprakis³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Thus, the probability reconstruction is put forward based on QR‐DNN and kernel density estimation (KDE). The probability reconstruction can depict the full distributions and facilitate the full understanding for the uncertainty of extreme values, helping planners in making effective decisions for structural design 25–27 …”

Section: Introductionmentioning

confidence: 99%

Nonstationary wind speed data reconstruction based on secondary correction of statistical characteristics

Lin

2021

Structural Contr & Hlth

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Summary Data reconstruction is usually used for lost data recovery and data supplement in structural health monitoring (SHM) system. In this paper, a data reconstruction algorithm is proposed for the entire time series data missing at dispersed and consecutive multiple observation points. Kriging‐based sequence interpolation (KSI) is adopted for primary data reconstruction and probability density function (PDF) reconstruction. In terms of dispersed data missing, the reconstructed PDF is of great accuracy, which is used to calculate the exact standard deviation to correct the primary reconstructed data and obtain satisfactory performance. Regarding the consecutive data missing, the first correction is insufficient due to the missing of too much information. In addition, it is found to exist a fuzzy mapping relation between the exact standard deviation and extreme values of wind speed series. Thus, the quantile regression combined with deep neural network is proposed for probability reconstruction of the extreme values. The point estimations are adopted to the secondary correction for the reconstructed data with first correction. The effectiveness and validity of the secondary correction strategy have been testified by structural dynamic response analysis. The extreme value of the displacement response under the reconstructed data with the secondary correction is closer to that under the actual wind data, compared with the reconstructed data with first correction.

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“…However, this efficiency improvement will not be enough to balance the expected growth in demand for energy in the short term [4,5]. This energy demand increase will be based on how quickly economies develop and societies' desire for a better way of life [6] through new electrical household equipment or full or hybrid electric transport [4,7]. Moreover, both the increase in energy demand and renewable self-supply installations will increase the volatility in energy demand, making activities carried out by the current power systems' controllers, such as real-time dispatching or stochastic unit commitment, more uncertain [8].…”

Section: Introductionmentioning

confidence: 99%

A Very Short-Term Probabilistic Prediction Interval Forecaster for Reducing Load Uncertainty Level in Smart Grids

et al. 2021

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Very short-term load demand forecasters are essential for power systems’ decision makers in real-time dispatching. These tools allow traditional network operators to maintain power systems’ safety and stability and provide customers energy with high reliability. Although research has traditionally focused on developing point forecasters, these tools do not provide complete information because they do not estimate the deviation between actual and predicted values. Therefore, the aim of this paper is to develop a very short-term probabilistic prediction interval forecaster to reduce decision makers’ uncertainty by computing the predicted value’s upper and lower bounds. The proposed forecaster combines an artificial intelligence-based point forecaster with a probabilistic prediction interval algorithm. First, the point forecaster predicts energy demand in the next 15 min and then the prediction interval algorithm calculates the upper and lower bounds with the user’s chosen confidence level. To examine the reliability of proposed forecaster model and resulting interval sharpness, different error metrics, such as prediction interval coverage percentage and a skill score, are computed for 95, 90, and 85% confidence intervals. Results show that the prediction interval coverage percentage is higher than the confidence level in each analysis, which means that the proposed model is valid for practical applications.

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Day-ahead short-term load probability density forecasting method with a decomposition-based quantile regression forest

Cited by 88 publications

References 36 publications

Impact of the COVID-19 Lockdown on the Electricity System of Great Britain: A Study on Energy Demand, Generation, Pricing and Grid Stability

Impact of the COVID-19 Lockdown on the Electricity System of Great Britain: A Study on Energy Demand, Generation, Pricing and Grid Stability

Nonstationary wind speed data reconstruction based on secondary correction of statistical characteristics

A Very Short-Term Probabilistic Prediction Interval Forecaster for Reducing Load Uncertainty Level in Smart Grids

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