Application and research for electricity price forecasting system based on multi-objective optimization and sub-models selection strategy

Fu, Tonglin; Zhang, Shenghui; Wang, Chen

doi:10.1007/s00500-020-04888-7

Cited by 11 publications

(2 citation statements)

References 62 publications

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“…where α and α * are the variables linked with the constraints (dual-problem solving as described above), which can exhibit values greater than zero and less than the adjustment hyperparameter C. K(α i , α j ) refers to the kernel function application (well known as kernel trick), which satisfies Mercer's conditions and transforms the data with nonlinearity into higher-dimensional feature space where linear separation is possible [54].…”

Section: Support Vector Machine: Regression (Svr)mentioning

confidence: 99%

Deep and Machine Learning Models to Forecast Photovoltaic Power Generation

et al. 2023

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The integration and management of distributed energy resources (DERs), including residential photovoltaic (PV) production, coupled with the widespread use of enabling technologies such as artificial intelligence, have led to the emergence of new tools, market models, and business opportunities. The accurate forecasting of these resources has become crucial to decision making, despite data availability and reliability issues in some parts of the world. To address these challenges, this paper proposes a deep and machine learning-based methodology for PV power forecasting, which includes XGBoost, random forest, support vector regressor, multi-layer perceptron, and LSTM-based tuned models, and introduces the ConvLSTM1D approach for this task. These models were evaluated on the univariate time-series prediction of low-volume residential PV production data across various forecast horizons. The proposed benchmarking and analysis approach considers technical and economic impacts, which can provide valuable insights for decision-making tools with these resources. The results indicate that the random forest and ConvLSTM1D model approaches yielded the most accurate forecasting performance, as demonstrated by the lowest RMSE, MAPE, and MAE across the different scenarios proposed.

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Section: Support Vector Machine: Regression (Svr)mentioning

confidence: 99%

Deep and Machine Learning Models to Forecast Photovoltaic Power Generation

et al. 2023

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“…where α and α * are the dual variables associated with the constraints that can take values greater than zero and less than the penalty hyperparameter C, K(α i , α j ) corresponds to the application of a kernel function (well-known as kernel trick) that satisfies Mercer's conditions and transforms the nonlinear data into a higher dimensional feature space to make a linear separation possible [29]. For this case, a Gaussian radial basis function (G-RBF) [30] as expressed in Equation ( 5) was used:…”

Section: Support Vector Machine: Regression (Svr)mentioning

confidence: 99%

Locational Marginal Price Forecasting Using SVR-Based Multi-Output Regression in Electricity Markets

et al. 2022

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Electricity markets provide valuable data for regulators, operators, and investors. The use of machine learning methods for electricity market data could provide new insights about the market, and this information could be used for decision-making. This paper proposes a tool based on multi-output regression method using support vector machines (SVR) for LMP forecasting. The input corresponds to the active power load of each bus, in this case obtained through Monte Carlo simulations, in order to forecast LMPs. The LMPs provide market signals for investors and regulators. The results showed the high performance of the proposed model, since the average prediction error for fitting and testing datasets of the proposed method on the dataset was less than 1%. This provides insights into the application of machine learning method for electricity markets given the context of uncertainty and volatility for either real-time and ahead markets.

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Wind speed forecast based on combined theory, multi-objective optimisation, and sub-model selection

Zhang

2022

Soft Comput

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Wind energy is the primary energy source for a sustainable and pollution-free global power supply. However, because of its characteristic irregularity, nonlinearity, non-stationarity, randomness, and intermittency, previous studies have only focused on stability or accuracy, and the forecast performances of their models were poor. Moreover, in previous research, the selection of sub-models used for the combined model was not considered, which weakened the generalisability. Therefore, to further improve the forecast accuracy and stability of the wind speed forecasting model, and to solve the problem of sub-model selection in the combined model, this study developed a wind speed forecasting model using data preprocessing, a multi-objective optimisation algorithm, and sub-model selection for the combined model. Simulation experiments showed that our combined model not only improved the forecasting accuracy and stability but also chose different sub-models and different weights of the combined model for different data; this improved the model generalisability. Speci cally, the MAPEs of our model are less than 4.96%, 4.60% and 5.25% in one, two and three step forecast.Thus, the proposed combined model is demonstrated as an effective tool for grid dispatching.

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Application and research for electricity price forecasting system based on multi-objective optimization and sub-models selection strategy

Cited by 11 publications

References 62 publications

Deep and Machine Learning Models to Forecast Photovoltaic Power Generation

Deep and Machine Learning Models to Forecast Photovoltaic Power Generation

Locational Marginal Price Forecasting Using SVR-Based Multi-Output Regression in Electricity Markets

Wind speed forecast based on combined theory, multi-objective optimisation, and sub-model selection

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