Multi-Step-Ahead Electricity Price Forecasting Based on Temporal Graph Convolutional Network

Su, Haokun; Peng, Xiangang; Liu, Hanyu; Quan, Huan; Wu, Kaitong; Chen, Zhiwen

doi:10.3390/math10142366

Cited by 11 publications

(3 citation statements)

References 37 publications

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“…In recent years, hybrid machine learning models (MLMs) became preferred over standalone models and are successfully applied in the difference fields to model different variables; e.g., for load forecasting [31], to estimate the international airport freight volumes [32], to predict electricity prices [33], and for modeling of the tensile strength of the concrete [34]. Due to the nonlinear nature of hydrological variables' time series, researchers found more precise and accurate results by utilizing hybrid MLMs than standalone MLMs.…”

Section: Introductionmentioning

confidence: 99%

Covariance Matrix Adaptation Evolution Strategy for Improving Machine Learning Approaches in Streamflow Prediction

et al. 2022

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Precise streamflow estimation plays a key role in optimal water resource use, reservoirs operations, and designing and planning future hydropower projects. Machine learning models were successfully utilized to estimate streamflow in recent years In this study, a new approach, covariance matrix adaptation evolution strategy (CMAES), was utilized to improve the accuracy of seven machine learning models, namely extreme learning machine (ELM), elastic net (EN), Gaussian processes regression (GPR), support vector regression (SVR), least square SVR (LSSVR), extreme gradient boosting (XGB), and radial basis function neural network (RBFNN), in predicting streamflow. The CMAES was used for proper tuning of control parameters of these selected machine learning models. Seven input combinations were decided to estimate streamflow based on previous lagged temperature and streamflow data values. For numerical prediction accuracy comparison of these machine learning models, six statistical indexes are used, i.e., relative root mean squared error (RRMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), Nash–Sutcliffe efficiency (NSE), and the Kling–Gupta efficiency agreement index (KGE). In contrast, this study uses scatter plots, radar charts, and Taylor diagrams for graphically predicted accuracy comparison. Results show that SVR provided more accurate results than the other methods, especially for the temperature input cases. In contrast, in some streamflow input cases, the LSSVR and GPR were better than the SVR. The SVR tuned by CMAES with temperature and streamflow inputs produced the least RRMSE (0.266), MAE (263.44), and MAPE (12.44) in streamflow estimation. The EN method was found to be the worst model in streamflow prediction. Uncertainty analysis also endorsed the superiority of the SVR over other machine learning methods by having low uncertainty values. Overall, the SVR model based on either temperature or streamflow as inputs, tuned by CMAES, is highly recommended for streamflow estimation.

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Section: Introductionmentioning

confidence: 99%

Covariance Matrix Adaptation Evolution Strategy for Improving Machine Learning Approaches in Streamflow Prediction

et al. 2022

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“…Each dense layer segment presents the number of units corresponding to the 8 h ahead prediction horizon, considering prediction, upper, and lower bounds, respectively. This model architecture requirement is critical, since for stakeholders and related decision-making tools, the information obtained in a multi-stepahead prediction is more valuable than a single-step-ahead prediction [73].…”

Section: Time2vec-transformer Model (T2v-te)mentioning

confidence: 99%

An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture

Cantillo-Luna,

Moreno-Chuquen,

Lopez-Sotelo

et al. 2023

Energies

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This paper describes the development of a deep neural network architecture based on transformer encoder blocks and Time2Vec layers for the prediction of electricity prices several steps ahead (8 h), from a probabilistic approach, to feed future decision-making tools in the context of the widespread use of intra-day DERs and new market perspectives. The proposed model was tested with hourly wholesale electricity price data from Colombia, and the results were compared with different state-of-the-art forecasting baseline-tuned models such as Holt–Winters, XGBoost, Stacked LSTM, and Attention-LSTM. The findings show that the proposed model outperforms these baselines by effectively incorporating nonlinearity and explicitly modeling the underlying data’s behavior, all of this under four operating scenarios and different performance metrics. This allows it to handle high-, medium-, and low-variability scenarios while maintaining the accuracy and reliability of its predictions. The proposed framework shows potential for significantly improving the accuracy of electricity price forecasts, which can have significant benefits for making informed decisions in the energy sector.

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“…It is fast in computation but poor in accuracy. The information of second-order or third-order neighbor nodes is further integrated to improve the accuracy 6 , which, however, increases the time complexity. The K-shell measures the importance of nodes by their location information in the network, and recursively deletes nodes with the same degree value.…”

Section: Introductionmentioning

confidence: 99%

Excavating important nodes in complex networks based on the heat conduction model

Hu,

Zheng,

et al. 2024

Sci Rep

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Analyzing the important nodes of complex systems by complex network theory can effectively solve the scientific bottlenecks in various aspects of these systems, and how to excavate important nodes has become a hot topic in complex network research. This paper proposes an algorithm for excavating important nodes based on the heat conduction model (HCM), which measures the importance of nodes by their output capacity. The number and importance of a node’s neighbors are first used to determine its own capacity, its output capacity is then calculated based on the HCM while considering the network density, distance between nodes, and degree density of other nodes. The importance of the node is finally measured by the magnitude of the output capacity. The similarity experiments of node importance, sorting and comparison experiments of important nodes, and capability experiments of multi-node infection are conducted in nine real networks using the Susceptible-Infected-Removed model as the evaluation criteria. Further, capability experiments of multi-node infection are conducted using the Independent cascade model. The effectiveness of the HCM is demonstrated through a comparison with eight other algorithms for excavating important nodes.

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Multi-Step-Ahead Electricity Price Forecasting Based on Temporal Graph Convolutional Network

Cited by 11 publications

References 37 publications

Covariance Matrix Adaptation Evolution Strategy for Improving Machine Learning Approaches in Streamflow Prediction

Covariance Matrix Adaptation Evolution Strategy for Improving Machine Learning Approaches in Streamflow Prediction

An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture

Excavating important nodes in complex networks based on the heat conduction model

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