National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?

Lee, Juyong; Cho, Youngsang

doi:10.1016/j.energy.2021.122366

Cited by 72 publications

(31 citation statements)

References 88 publications

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“…The paper presents an approach to predict Photovoltaic (PV) generation under different weather conditions. Statistical and intelligent models based on machine learning used for PV generation forecasting were also reported in [ 3 , 59 , 60 , 61 ].…”

Section: Related Workmentioning

confidence: 99%

Short-term renewable energy consumption and generation forecasting: A case study of Western Australia

Abu-Salih

Wongthongtham

Morrison

et al. 2022

Heliyon

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Section: Related Workmentioning

confidence: 99%

Short-term renewable energy consumption and generation forecasting: A case study of Western Australia

Abu-Salih

Wongthongtham

Morrison

et al. 2022

Heliyon

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“…We consider a data set of the high-resolution peak load estimation challenge ranging from November 2019 to September 2021, covering almost two years of data. The data was provided by WPD 1 . Next to the mentioned load data that is also illustrated in Fig.…”

Section: Setting Data and Exploratory Analysismentioning

confidence: 99%

“…2. This weather data includes temperature (temp), solar irradiance (solar), wind speed north (windN), wind speed east (windE), 1 The data can be retreived from WPD Datasets pressure (press), and specific humidity (humid). We utilize the six weather inputs by averaging across the five locations.…”

Section: Setting Data and Exploratory Analysismentioning

confidence: 99%

“…While nationwide forecasts are essential for climate change issues and resource planning, Network operators are generally more interested in regional forecasts. Although research on forecasting nationwide peak-load emerged recently [1], the majority of research focuses on predicting regional peak-loads. However, there is also research on peak-load shaving [2], another relevant issue for network operators.…”

Section: Introductionmentioning

confidence: 99%

“…There are various papers dealing with peak-load prediction at different scales, e.g, forecasting short-term load of individual households [3], [4], office buildings [5], low-voltage feeders [6], cities [7], and nations [1]. The results indicate that weather data like temperature, humidity, wind speeds, and air-pressure help explain peak-loads [8]- [10].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

High-Resolution Peak Demand Estimation Using Generalized Additive Models and Deep Neural Networks

Berrisch¹,

Narajewski²,

Ziel³

2022

Preprint

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This paper presents a method for estimating highresolution electricity peak demand given lower resolution data. The technique won a data competition organized by the British distribution network operator Western Power Distribution. The exercise was to estimate the minimum and maximum load values in a single substation in a one-minute resolution as precisely as possible. In contrast, the data was given in half-hourly and hourly resolutions. The winning method combines generalized additive models (GAM) and deep artificial neural networks (DNN) which are popular in load forecasting. We provide an extensive analysis of the prediction models, including the importance of input parameters with a focus on load, weather, and seasonal effects. In addition, we provide a rigorous evaluation study that goes beyond the competition frame to analyze the robustness. The results show that the proposed methods are superior, not only in the single competition month but also in the meaningful evaluation study.

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Probabilistic electricity price forecasting based on penalized temporal fusion transformer

Jiang,

Pan,

Dong

et al. 2024

Journal of Forecasting

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AbstactIn the deregulated electricity market, it is increasingly important to accurately predict the fluctuating, nonlinear, and high‐frequent electricity price for market decision‐making. However, the uncertainties associated with electricity prices, such as non‐stationarity, nonlinearity, and high volatility, pose critical difficulties for electricity price forecasting (EPF). Unlike point forecasting, which provides only a single, deterministic estimate of future prices, probabilistic forecasting gives a more comprehensive and nuanced picture of future price dynamics, which can help market participants make better‐informed decisions when facing uncertainty. Therefore, in this paper, we propose a robust deep learning method for multi‐step probabilistic forecasting. First, we use the least absolute shrinkage and selection operator (LASSO) in the expert model to generate point forecasts. Second, we introduce the smoothly clipped absolute deviation regularization term, a nonconvex penalty with proven oracle properties in model selection, into temporal fusion transformers. Finally, we employ the proposed model to integrate point forecasts to give probabilistic forecasts. To evaluate the proposed forecasting model, real‐data experiments are conducted in the Nord Pool electricity market and the Polish Power Exchange market. Empirical results show that the proposed model has demonstrated superior probabilistic forecasting performances compared with other competitors and has proven its effectiveness in real‐world applications.

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National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?

Cited by 72 publications

References 88 publications

Short-term renewable energy consumption and generation forecasting: A case study of Western Australia

Short-term renewable energy consumption and generation forecasting: A case study of Western Australia

High-Resolution Peak Demand Estimation Using Generalized Additive Models and Deep Neural Networks

Probabilistic electricity price forecasting based on penalized temporal fusion transformer

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