Probabilistic load forecasting for the low voltage network: Forecast fusion and daily peaks

Gilbert, Ciaran; Browell, Jethro; Stephen, Bruce

doi:10.1016/j.segan.2023.100998

Cited by 9 publications

(3 citation statements)

References 40 publications

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“…Literature on load and peak forecasting can be clustered according to time, into short, medium and long term [34]. Another distinction can be made in the following categories: a) the conditional modeling approach, generally based on macroeconomic variables like inflation, GDP, Forex, etc.. [8][9][10][11][12], b) the system indicators of the electrical distribution and transmission system, such as the number of connections, machinery capacity etc., [13][14][15][16][17][18], c) the historical modeling approach [9,19] and d) hybrid models [20,21]. Finally, literature can be clustered around the method used, a distinction used by Weron [22], in the disciplines of a) time series analysis-statistics [8,21,23,24], b) informatics or computational intelligence and c) hybrid models [25][26][27][28].…”

Section: Literature Reviewmentioning

confidence: 99%

Electricity Demand, Forecasting the Peaks: Development and Implementation of C-EVA Method

Theodorou,

Christopoulos

2024

GLCE

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Price spikes in electricity markets are very frequent, posing tremendous burden on household income and on manufacturing cost. Electricity demand (load) can be divided in two parts, energy (MWh) and peak (MW) and most of time peak is responsible for the price spikes. Literature review while devoting most of the discussion to energy, lags in the investigation of peak. In this research, a model for peak demand analysis and forecasting is developed. The model is based on a portfolio of cluster and extreme value analysis (C-EVA) methods using UIK, EDE and WSL innovations for the optimal determination of clusters and the daily peaks divulgence. C-EVA method consists of the Clustering part for optimal number of clusters determination and classification of day and month of peak, and the part of Extreme Value Analysis for computation of the statistical confidence interval for the load maxima. C-EVA after using all the currently available load maxima, estimates statistically the expected worst-case scenario for peaks of loads. Load peaks will be determined by EVA based on an estimated bimodal distribution while a signaling method will prompt the probability of extremes. The added value of the proposed method is that does not reject the extreme values as most methodologies do. Extreme Value Analysis for maxima and minima provide estimators for highest and lowest expected hourly load, while giving the confidence interval of the return level using an optimization method for the selection of a rolling time window, as the return period. It was found that distributed generation of renewables create a camel effect on the load peaks which increases sharpness. The proposed methodology solved this issue while opening the ground for future research for the role of storage, batteries as well as for virtual power plants as an integrated portfolio of renewables generation.

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Section: Literature Reviewmentioning

confidence: 99%

Electricity Demand, Forecasting the Peaks: Development and Implementation of C-EVA Method

Theodorou,

Christopoulos

2024

GLCE

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“…The authors reported a mixed performance, particularly in peak timing forecasting, especially when employing a multi-resolution generalized additive model. Furthermore, another study proposed employing probabilistic forecasting with forecast fusion in the low-voltage network to predict peak density and timing [42]. Their proposed framework underwent testing using real smart meter data and a hypothetical low-voltage network hierarchy comprising feeders as well as secondary and primary substations.…”

Section: Peak Demand Predictionmentioning

confidence: 99%

Toward Prediction of Energy Consumption Peaks and Timestamping in Commercial Supermarkets Using Deep Learning

Zhao,

Gomez-Rosero,

Nouraei

et al. 2024

Energies

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Building energy consumption takes up over 30% of global final energy use and 26% of global energy-related emissions. In addition, building operations represent nearly 55% of global electricity consumption. The management of peak demand plays a crucial role in optimizing building electricity usage, consequently leading to a reduction in carbon footprint. Accurately forecasting peak demand in commercial buildings provides benefits to both the suppliers and consumers by enhancing efficiency in electricity production and minimizing energy waste. Precise predictions of energy peaks enable the implementation of proactive peak-shaving strategies, the effective scheduling of battery response, and an enhancement of smart grid management. The current research on peak demand for commercial buildings has shown a gap in addressing timestamps for peak consumption incidents. To bridge the gap, an Energy Peaks and Timestamping Prediction (EPTP) framework is proposed to not only identify the energy peaks, but to also accurately predict the timestamps associated with their occurrences. In this EPTP framework, energy consumption prediction is performed with a long short-term memory network followed by the timestamp prediction using a multilayer perceptron network. The proposed framework was validated through experiments utilizing real-world commercial supermarket data. This evaluation was performed in comparison to the commonly used block maxima approach for indexing. The 2-h hit rate saw an improvement from 21% when employing the block maxima approach to 52.6% with the proposed EPTP framework for the hourly resolution. Similarly, the hit rate increased from 65.3% to 86% for the 15-min resolution. In addition, the average minute deviation decreased from 120 min with the block maxima approach to 62 min with the proposed EPTP framework with high-resolution data. The framework demonstrates satisfactory results when applied to high-resolution data obtained from real-world commercial supermarket energy consumption.

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“…While the majority of recent works have focused on point forecasting, [12] consider adaptive probabilistic load forecasting based on hidden Markov models and is found to perform well for a range of loads; however, this approach relies on Gaussian predictive distributions which leads to relatively poor calibration (quantile bias) on the data we consider here. In [13], [14], quantile GAMs are proposed, as well as variants of GAMLSS (Location Scale and Shape) in [15], for probabilistic demand forecasting. These methods relax the Gaussian assumption but are not adaptive.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Probabilistic Forecasting of Electricity (Net-)Load

de Vilmarest,

Browell,

Fasiolo

et al. 2024

IEEE Trans. Power Syst.

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Electricity load forecasting is a necessary capability for power system operators and electricity market participants. Both demand and supply characteristics evolve over time. On the demand side, unexpected events as well as longer-term changes in consumption habits affect demand patterns. On the production side, the increasing penetration of intermittent power generation significantly changes the forecasting needs. We address this challenge in two ways. First, our setting is adaptive; our models take into account the most recent observations available to automatically respond to changes in the underlying process. Second, we consider probabilistic rather than point forecasting; indeed, uncertainty quantification is required to operate electricity systems efficiently and reliably. Our methodology relies on the Kalman filter, previously used successfully for adaptive point load forecasting. The probabilistic forecasts are obtained by quantile regressions on the residuals of the point forecasting model. We achieve adaptive quantile regressions using the online gradient descent; we avoid the choice of the gradient step size considering multiple learning rates and aggregation of experts. We apply the method to two data sets: the regional net-load in Great Britain and the demand of seven large cities in the United States. Adaptive procedures improve forecast performance substantially in both use cases for both point and probabilistic forecasting.

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Probabilistic load forecasting for the low voltage network: Forecast fusion and daily peaks

Cited by 9 publications

References 40 publications

Electricity Demand, Forecasting the Peaks: Development and Implementation of C-EVA Method

Electricity Demand, Forecasting the Peaks: Development and Implementation of C-EVA Method

Toward Prediction of Energy Consumption Peaks and Timestamping in Commercial Supermarkets Using Deep Learning

Adaptive Probabilistic Forecasting of Electricity (Net-)Load

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