Research and application of a hybrid model for mid-term power demand forecasting based on secondary decomposition and interval optimization

Niu, Dongxiao; Ji, Zhengsen; Li, Wanying; Xu, Xiaomin; Li, Da

doi:10.1016/j.energy.2021.121145

Cited by 35 publications

(11 citation statements)

References 52 publications

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“…In recent years, with the continuous development of neural networks, a growing number of scholars have begun to apply neural networks into power demand forecasting [36], and frequently used models include the gated recurrent unit (GRU) [37], long short-term memory networks (LSTM) [38], and the temporal convolutional network (TCN) [39], etc. In order to verify the advancement and effectiveness of the XLG-LR model, the power demand data of this paper was used to train the above GRU, LSTM, TCN models and the XLG-LR model, and utilized the test set to test the training results.…”

Section: Discussionmentioning

confidence: 99%

Short- and Medium-Term Power Demand Forecasting with Multiple Factors Based on Multi-Model Fusion

Zhang

Duan

et al. 2022

Mathematics

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With the continuous development of economy and society, power demand forecasting has become an important task of the power industry. Accurate power demand forecasting can promote the operation and development of the power supply industry. However, since power consumption is affected by a number of factors, it is difficult to accurately predict the power demand data. With the accumulation of data in the power industry, machine learning technology has shown great potential in power demand forecasting. In this study, gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM) are integrated by stacking to build an XLG-LR fusion model to predict power demand. Firstly, preprocessing was carried out on 13 months of electricity and meteorological data. Next, the hyperparameters of each model were adjusted and optimized. Secondly, based on the optimal hyperparameter configuration, a prediction model was built using the training set (70% of the data). Finally, the test set (30% of the data) was used to evaluate the performance of each model. Mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and goodness-of-fit coefficient (R^2) were utilized to analyze each model at different lengths of time, including their seasonal, weekly, and monthly forecast effect. Furthermore, the proposed fusion model was compared with other neural network models such as the GRU, LSTM and TCN models. The results showed that the XLG-LR model achieved the best prediction results at different time lengths, and at the same time consumed the least time compared to the neural network model. This method can provide a more reliable reference for the operation and dispatch of power enterprises and future power construction and planning.

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Section: Discussionmentioning

confidence: 99%

Short- and Medium-Term Power Demand Forecasting with Multiple Factors Based on Multi-Model Fusion

Zhang

Duan

et al. 2022

Mathematics

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“…In [23], the optimized ANNs by particle swarm optimization approach is suggested for MTLF, and by various examinations, the reduction of the number of input variables is studied. In [24], an ANN approach is developed, and the elimination of climate variables from the list of input factors is investigated. In addition, the effect of population growth is investigated by the designed ANN model.…”

Section: Literature Reviewmentioning

confidence: 99%

A New Deep Learning Restricted Boltzmann Machine for Energy Consumption Forecasting

Tian

Firouzi

et al. 2022

Sustainability

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A key issue in the desired operation and development of power networks is the knowledge of load growth and electricity demand in the coming years. Mid-term load forecasting (MTLF) has an important rule in planning and optimal use of power systems. However, MTLF is a complicated problem, and a lot of uncertain factors and variables disturb the load consumption pattern. This paper presents a practical approach for MTLF. A new deep learning restricted Boltzmann machine (RBM) is proposed for modelling and forecasting energy consumption. The contrastive divergence algorithm is presented for tuning the parameters. All parameters of RBMs, the number of input variables, the type of inputs, and also the layer and neuron numbers are optimized. A statistical approach is suggested to determine the effective input variables. In addition to the climate variables, such as temperature and humidity, the effects of other variables such as economic factors are also investigated. Finally, using simulated and real-world data examples, it is shown that for one year ahead, the mean absolute percentage error (MAPE) for the load peak is less than 5%. Moreover, for the 24-h pattern forecasting, the mean of MAPE for all days is less than 5%.

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“…The optimal solution of fuzzy programming should be obtained based on empirical probability and reasonable fuzzy membership function. Reference (Zhang et al, 2019;Niu et al, 2021;Zeng et al, 2021;Jiang et al, 2022) adopts the interval optimization method to consider the uncertainty of renewable energy and load demand. Interval optimization does not need to assume the probability distribution of uncertain variables, but needs to choose a reasonable confidence interval.…”

Section: Introductionmentioning

confidence: 99%

“…Information gap decision theory (IGDT) is a non-probabilistic risk assessment method, which can link the prediction deviation with the optimal objective function to maximize the uncertainty variable disturbance while ensuring the lowest objective value. Reference (CAO et al, 2018;Ye et al, 2021) realized multi-source joint dispatching of power systems based on information gap theory, and carried out microgrid operation planning; Reference (Peng et al, 2020;Niu et al, 2021) studies the impact of renewable energy output based on classification probability opportunity constraint information gap decision theory on distribution network energy storage configuration effect; Reference (Li et al, 2019;Li et al, 20222022), based on information gap theory, studied the stochastic optimal scheduling strategy of integrated energy system considering carbon trading mechanism and carbon capture.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimal dispatching of virtual power plants considering source-load uncertainty in V2G mode

et al. 2023

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To solve the risks brought by the uncertainty of renewable energy output and load demand to the virtual power plant dispatch, a multi-objective information gap decision theory (IGDT) dispatching model for virtual power plants considering source-load uncertainty under vehicle-to-grid (V2G) is proposed. With the lowest system operating cost and carbon emission as the optimization objectives, the multi-objective robust optimization model for virtual power plants is constructed based on the uncertainties of wind output, photovoltaic output and load demand guided by the time of use price. The weights of uncertainties quantify the effects of uncertainty factors. The adaptive reference vector based constrained multi-objective evolutionary algorithm is used to solve it. The weight coefficients, evasion coefficients of uncertainties and the penetration rate of electric vehicles are analyzed for the optimal dispatching of the virtual power plant. The algorithm results show that the method can effectively achieve load-side peak shaving and valley filling and has superiority in terms of economy, environmental benefits, robustness and stability.

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Research and application of a hybrid model for mid-term power demand forecasting based on secondary decomposition and interval optimization

Cited by 35 publications

References 52 publications

Short- and Medium-Term Power Demand Forecasting with Multiple Factors Based on Multi-Model Fusion

Short- and Medium-Term Power Demand Forecasting with Multiple Factors Based on Multi-Model Fusion

A New Deep Learning Restricted Boltzmann Machine for Energy Consumption Forecasting

Multi-objective optimal dispatching of virtual power plants considering source-load uncertainty in V2G mode

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