Improving economic values of day‐ahead load forecasts to real‐time power system operations

Wang, Yamin; Wu, Lei

doi:10.1049/iet-gtd.2017.0517

Cited by 17 publications

(5 citation statements)

References 41 publications

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“…Traditionally, the regression models for deterministic load forecasting are trained using the metric of mean square error (MSE) loss function, with the implication that forecasting errors, identical in magnitude, cause the same and quadratic costs. Apparently, this assumption is not accurate, especially for cases where asymmetric costs are observed for forecasting errors in the same magnitude but with opposite sign [14], [15], [16], [17], [18]. Thus, the forecasting errors are not able to precisely quantify the economic value among different forecasting models.…”

Section: Introductionmentioning

confidence: 99%

“…On this basis, a genetic algorithm (GA) was applied to update the parameters of the radial basis function (RBF) network with a discontinuous and non-symmetric loss function. Similarly, an asymmetric error penalty function was designed in [16] based on the simulation results from the day-ahead unit commitment (DAUC) problem. The derived non-differentiable penalty function was optimized through GA for the combined backpropagation and RBF neural networks.…”

Section: Introductionmentioning

confidence: 99%

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Cost-Oriented Load Forecasting

Zhang¹,

Yi²,

Hug³

2021

Preprint

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Accurate load prediction is an effective way to reduce power system operation costs. Traditionally, the mean square error (MSE) is a common-used loss function to guide the training of an accurate load forecasting model. However, the MSE loss function is unable to precisely reflect the real costs associated with forecasting errors because the cost caused by forecasting errors in the real power system is probably neither symmetric nor quadratic. To tackle this issue, this paper proposes a generalized cost-oriented load forecasting framework. Specifically, how to obtain a differentiable loss function that reflects real cost and how to integrate the loss function with regression models are studied. The economy and effectiveness of the proposed load forecasting method are verified by the case studies of an optimal dispatch problem that is built on the IEEE 30-bus system and the open load dataset from the Global Energy Forecasting Competition 2012 (GEFCom2012).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cost-Oriented Load Forecasting

Zhang¹,

Yi²,

Hug³

2021

Preprint

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“…There has been some research on power load forecasting in the energy field, including power system load forecasting [5], distributed photovoltaic load forecasting [6], wind power load forecasting [7], etc. Power load has a certain regularity and a certain randomness.…”

Section: Introductionmentioning

confidence: 99%

Power Load Forecasting Considering Climate Factors Based on IPSO-Elman Method in China

Liu

Yin

2022

Energies

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In order to implement the national need for the optimal allocation of power resources, power load forecasting, as an important research topic, has important theoretical and practical significance. The purpose of this study is to construct a prediction model considering climate factors based on a large amount of historical data, and to prove that the prediction accuracy is related to both climate factors and load regularity. The results of load forecasting are affected by many climate factors, so firstly the climate variables affecting load forecasting are screened. Secondly, a load prediction model based on the IPSO-Elman network learning algorithm is constructed by taking the difference between the predicted value of the neural network and the actual value as the fitness function of particle swarm optimization. In view of the great influence of weights and thresholds on the prediction accuracy of the Elman neural network, the particle swarm optimization algorithm (PSO) is used to optimize parameters in order to improve the prediction accuracy of ELMAN neural network. Thirdly, prediction with and without climate factors is compared and analyzed, and the prediction accuracy of the model compared by using cosine distance and various error indicators. Finally, the stability discriminant index of historical load regularity is introduced to prove that the accuracy of the prediction model is related to the regularity of historical load in the forecast area. The prediction method proposed in this paper can provide reference for power system scheduling.

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“…To solve this problem, load forecasting data is utilized to compute UC; therefore, the accuracy of load forecasting is very important for UC solution [2]. Y. Wang and L. Wu [3] show the important role of load forecasting on UC. The result demonstrates that a 1 % reduction in the average forecast error of STLF saves even millions of dollars.…”

Section: Introductionmentioning

confidence: 99%

A Novel Short-Term Load Forecasting Method by Combining the Deep Learning With Singular Spectrum Analysis

Pham

Nguyen

2021

IEEE Access

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One of the major issues about the operation of power systems is the prediction of load demand. Moreover, load forecasting is of prime concern to system operators. Recently, the integration of power system elements, such as renewable energy sources, energy storages and electricity vehicle, brings more challenges, particularly when there are large fluctuations in forecasting cycle. This study concentrates on short-term load demand forecasting and proposes a hybrid method that combines Singular Spectrum Analysis (SSA) with deep-learning Neural Network (NN) techniques. In the beginning, the SSA technique is applied as an initial filter to remove noises. Next, a hybrid neural network, including Backpropagation Neural Network (BPNN) and Long Short-Term Memory (LSTM), is developed and trained. Then, the trained network is used as the core forecasting algorithm. Each SSA has different forms to combine with neural networks. The performance of the proposed forecasting algorithm is demonstrated using the power demand data recorded in Taiwan. Furthermore, this study compares the forecasting results by five models, including SSA, SSA-BPNN, ANN, SSA-LSTM, Wavelet Neural Network (WNN) and LSTM. The forecasting results reveal that the proposed forecasting model using Singular Spectrum Analysis provides the best performance on load forecasts.

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Improving economic values of day‐ahead load forecasts to real‐time power system operations

Cited by 17 publications

References 41 publications

Cost-Oriented Load Forecasting

Cost-Oriented Load Forecasting

Power Load Forecasting Considering Climate Factors Based on IPSO-Elman Method in China

A Novel Short-Term Load Forecasting Method by Combining the Deep Learning With Singular Spectrum Analysis

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