- Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin, China
Due to the strong coupling characteristics and daily correlation characteristics of multiple load sequences, the prediction method based on time series extrapolation and combined with multiple load meteorological data has limited accuracy improvement, which is tested by the fluctuation of load sequences and the accuracy of Numerical Weather Prediction (NWP). This paper proposes a multiple load prediction method considering the coupling characteristics of multiple loads and the division of load similar fluctuation sets. Firstly, the coupling characteristics of multivariate loads are studied to explore the interaction relationship between multivariate loads and find out the priority of multivariate load prediction. Secondly, the similar fluctuating sets of loads are divided considering the similarity and fluctuation of load sequences. Thirdly, the load scenarios are divided by k-means clustering for the inter-set sequences of similar fluctuating sets, and the Bi-directional Long Short-Term Memory (BI-LSTM) models are trained separately for the sub-set of scenarios and prioritized by prediction. Finally, the effectiveness of the proposed method was verified by combining the multivariate load data provided by the Campus Metabolism system of Arizona State University.
1 Introduction
Intergrated Energy System (IES) is a new energy system that integrates electricity, natural gas, heating and cooling energy supply, including various forms of energy production, energy conversion, energy distribution, energy storage and energy utilization, etc. (Valery et al., 2022). Compared with the traditional energy utilization system, IES can realize the coupling of different types of energy in different links such as source, network and load side, effectively improving the comprehensive utilization rate of energy (Wei et al., 2022). At present, as the basis for guiding the optimal scheduling of the system, IES load prediction is important for the accurate prediction of multivariate loads, considering the interaction of various relevant factors in IES and the complex mechanism (Wang et al., 2021; Jizhong et al., 2022).
On the energy supply side of the IES, the main focus is on wind power and PV power prediction. Among them, In Mao et al. (2022), proposed a composite prediction framework DC (DWT-DAE)-CNN consisting of dual clustering and convolutional neural network. Firstly, discrete wavelet transform (DWT) and deep self-encoder (DAE) are performed on the original data respectively to reduce data redundancy. Secondly, a pairwise clustering model based on dynamic time-bending distance clustering and fuzzy C-mean (FCM) clustering is proposed to gradually realize the dynamic characteristics of power curves and numerical clustering of weather information data. Finally, it is verified by arithmetic case analysis. In Mao et al. (2020), proposed an improved fuzzy (FCM) clustering algorithm, which can obtain better prediction results by using the principle of minimum distance to select relatively coarse initial cluster centers, and by dividing wind turbines with similar power output characteristics into several classes and selecting representative power curves as the equivalent curves of wind farms. The shortest distance method clustering is proposed to provide the initial clustering center for FCM, the use of validity analysis of the degree of similarity of samples within classes and the degree of independence between different classes to discriminate the superiority of clustering results, and the identification and elimination of noise points by data density are proposed to improve the performance of FCM clustering algorithm (Kai et al., 2022). In Yang et al. (2019), used set-pair analysis to assess the correlation between the power fluctuations of individual wind turbines and the power fluctuations of all aggregated wind turbines, and between the smoothing effect of aggregated wind farms and the prediction accuracy of the corresponding aggregated power output. The experimental results show that the wind power prediction accuracy varies with the smoothing effect index, which is influenced by the number of wind farms. In Wu et al. (2022a), proposed a multi-stage urban distribution network (UDN) resilience enhancement framework to cope with the substantial loss of UDN critical loads caused by high impact low probability events (HILP) in UDN. In the first stage, the distribution system operator forms typical failure scenarios based on historical data of electrical component damage under ice events and sets up specific response plans in each scenario to reduce the lost load and associated costs, in the second stage, the operator performs a risk assessment of the corresponding plans, and in the third stage, the operator revises the response plans to reduce the “second stage” impact. In Wu et al. (2021a), promotes distributed renewable energy consumption through a specially set price mechanism that incorporates supply and demand ratios into the dynamic price formation process to better accommodate highly penetrated renewable energy sources and small-scale energy markets. A two-way auction model is proposed in Wu et al. (2021b), first, to establish a participant-driven framework for distributed trading of electricity demand response, followed by a bargaining game for cost and benefit allocation, and finally, to form a co-optimization model for electricity and hydrogen considering production constraints to improve system capacity and economics. In Nantian et al. (2022), a multi-node charging load joint adversarial generation interval prediction method considering the charging load correlation between nodes is proposed to effectively predict the spatio-temporal distribution of EV charging load with respect to the time-space progressivity of EV charging load.
On the energy consumption side of the IES, the main focus is on multi-energy load prediction. Among them, a novel decomposition-ensemble model for short-term load prediction is proposed in Yang et al. (2019). (Xiaobo and Jianzhou, 2018) The singular spectrum analysis (SSA) decomposition and reconstruction strategy is introduced in the proposed model, and the cuckoo search algorithm is used to generate the ensemble results and thus improve the model prediction accuracy. In Abhishek et al. (2022), a seasonal partitioning method is proposed for day-ahead prediction of electrical loads, and corresponding prediction models are established for the transition season and the regular season, and in the transition season, the weighted output method of multiple seasonal prediction models is used to improve the prediction accuracy. In Mukhopadhyay et al. (2017), explore how to reasonably use meteorological factors for load prediction, use the meteorological factors of the day to determine the magnitude of load, and consider day type information to appropriately scale the forecast results for rest days to better approximate the actual load. In Luiz and Afshin (2015), a transfer function (TF) model was developed using measured hourly weather variables for the simulation and prediction of electric loads and compared with an autoregressive integrated moving average model (ARIMA) and an artificial neural network (ANN) based on exogenous variables, and finally, an arithmetic analysis concluded that the accuracy of the proposed method has good stability with the extension of the time series. The above research methods mainly focus on some single load type for prediction, compared to single load prediction, the research on multivariate load prediction is relatively new, and the multi-task structure is commonly used to accommodate multivariate output requirements. For example, a short-term prediction method for electricity and gas demand based on a radial basis function neural network (RBF-NN) model was proposed (Tang et al., 2019). A multivariate load prediction model based on kernel principal component analysis, quadratic modal decomposition, two-way LSTM and multiple linear regression was proposed (Jinpeng et al., 2021; Yingjun et al., 2022). In Ref. (Qingkai et al., 2021), a multi-task learning load prediction model with Long-Short-Term Memory (LSTM) as a shared layer is constructed, where the learning of single load features is first performed separately, and then the auxiliary coupling information is learned using the shared layer. In Ref. (Jixiang et al., 2019), a mixed-model short-term load prediction method based on convolutional neural networks and LSTM is proposed, in which a large amount of historical load data, meteorological data, date information, and peak and valley tariff data are used as inputs to construct a continuous fluctuation map by time-sliding windows, the eigenvectors are first extracted using convolutional neural networks. The eigenvectors are constructed in a time-series manner and used as input data for the LSTM network, and then the LSTM network is used for short-term load prediction. The method proposed in Ref. (Haohan et al., 2021), fails to explicitly consider the complex coupling interaction features between multi-energy loads, where LSTM as a recurrent neural network has its own limitations, and although it can better mine the data temporal features, it cannot fully explore the interaction coupling relationship between multi-energy loads. The key idea of load prediction is to exploit the cyclical nature of load demand behavior and its dependence on other influencing factors, such as multi-energy coupling and weather information. However, the analysis of historical data shows that some of these influences do not have a uniform impact on load demand throughout the year.
To sum up, this paper proposes a multivariate load ultra-short-term prediction method based on load similar fluctuation set division combined with multivariate load coupling characteristics. First, a gray correlation analysis is performed on the multivariate load series to explore the coupling characteristics of the multivariate load data. Secondly, based on the historical load data, the actual load sequence of the highest priority is divided into correlation and volatility to obtain the set of load similar fluctuations. Thirdly, the NWP strongly correlated features of inter-set load sequences are used as input for k-means clustering to classify load scenes and train BI-LSTM network by load scenes; Finally, the final prediction results are obtained by ranking the multivariate load prediction priorities one by one.
2 Coupling characteristics mining of multiple loads
Before carrying out the research work of IES multiple load prediction, the energy use characteristics of the system should be analyzed, i.e., starting from the mechanism of load composition and revealing the inherent change law of the system load itself. Multi-class energy coupling mechanism is the theoretical basis of IES, and all kinds of energy sources are interactively coupled with each other, so the coupling characteristics between all kinds of energy sources need to be considered in the process of multiple load prediction (Jizhong et al., 2021). Multi-Task Learning (MTL) is often used for joint prediction of multivariate loads. The idea is to use multiple types of loads as the object of study and other influencing factors as fluctuation data to predict multiple types of loads simultaneously, which is a method of multiple inputs accompanied by multiple outputs at the same time (Wang et al., 2021). Although this method is able to take into account the coupling characteristics between multiple types of energy sources, the output side of the method outputs the multivariate load forecasts simultaneously, and the coupling strengths between the various types of energy sources may be different.
Therefore, this paper takes the lead in exploring the coupling strength between multiple types of energy sources and analyzes the correlation between multiple load series by Grey Relation Analysis (GRA) method to portray the coupling strength between multiple loads by correlation degree (Xuexiang et al., 2022). The sum of the correlations between any two selected loads and the third load is set as a parameter indicator
3 Load similar fluctuation set division
The average daily load distribution within a season follows almost a similar pattern. However, the classification of load characteristics by season alone is too subjective and is likely to ignore the load characteristics between non-contiguous months. Based on the above multiple load coupling characteristics mining, this paper divides the highest priority electrical loads into load similar fluctuation sets and analyzes the monthly average electrical load distribution of the integrated energy system under the complete seasonal sequence as shown in Figure 1. It can be seen that similar fluctuations exist between different seasons.
Firstly, the average monthly electric load distribution series under different months is obtained by taking the average value of parallel load points of the load series under different load days in the same month. Secondly, the obtained daily average electric load distribution series are then compared two by two using grey correlation analysis to form a correlation coefficient matrix
The correlation threshold
Where
Let the minimum correlation of the sequence
Search for
4 K-means clustering method for each fluctuation set sequence
4.1 Gray correlation analysis method
Electricity, heat and cold loads in IES have strong correlation for meteorological factors, and the usual prediction method is to artificially and subjectively select several meteorological factors as inputs, which does not seem to be logically sufficiently justified. Therefore, before carrying out multi load prediction of the regional integrated energy system, the various types of loads and the correlation strength between them and meteorological factors should be analyzed and screened, so as to analyze the coupling characteristics among electric, heat and cold loads, as well as the impact of each influencing factor on the multi loads.
GRA compensates for the shortcomings caused by the use of mathematical and statistical methods for systematic analysis. GRA is a multi-factor statistical analysis method, and its basic idea is to determine whether the association is strong or not based on the similarity of the geometry of the series curves. The closer the curves are to the response series, the greater the degree of association between them, and vice versa. The GRA method is suitable for analyzing the nonlinear relationship between multiple loads and influencing factors, which can largely reduce the loss due to information asymmetry, and this method does not need a large number of data sets as the basis, the calculation is small and fast, and there is no discrepancy between quantitative results and qualitative analysis results. Therefore, in this paper, GRA is selected to quantitatively analyze the degree of influence of meteorological factors on the multiplicative load, and the meteorological factors that have the greatest influence on the multiplicative load are selected as the influence factors to analyze the correlation between each influence factor and the multiplicative load.
4.2 Multivariate load and meteorological factor correlation analysis
The correlation coefficient and the correlation degree of the GRA method are two key parameters used to measure the correlation, and the magnitude of the correlation degree can visually reflect the degree of association between two factors, which are calculated by the following equations.
Where
The correlation between the multiple loads of the integrated regional energy system and each meteorological influencing factor is analyzed. Let the sequence of electrical, heat and cold loads and each meteorological influence form the following matrix.
Based on the load prediction priorities derived from the above correlation analysis for multivariate loads, the NWP strongly correlated features are used as input for k-means clustering to classify load scenarios, and the BI-LSTM network is trained separately for each load scenario set for sub-scenario set prediction modeling.
Where
5 Multivariate load prediction model
5.1 LSTM neural network prediction model
LSTM is an improved model of Recurrent Neural Network (RNN), which solves the gradient disappearance and explosion problems of RNN, enabling the network to effectively handle long-term time series data, improving the ability to handle samples with long time series intervals or delays, as well as the ability to handle non-linear data (Hongbo et al., 2022). The network structure of a typical RNN is shown in Figure 3.
LSTM can learn long-term dependent information, the key of which is the transfer of cell states, and it has the same repetitive way as RNN, but redesigned memory units while retaining the original structure of RNN, The LSTM sets up three control gates, input gate
As shown in Figure 3, the output value
Where
The new information is then subjected to a sigmoid function to determine the data that needs to be input into the memory cell, while a new candidate state
Where
Then, update the cell state, first multiply the old state
In addition, the implied layer data values
Where
5.2 BI-LSTM prediction model
Both RNN and LSTM can only predict the output of the next moment based on the temporal information of the previous moment, but the output of the current moment is not only related to the previous state, but also may be related to the future state. BI-LSTM is a combination of a forward LSTM and a backward LSTM, and its architecture is shown in Figure 5. It can be viewed as a two-layer neural network, with the first layer serving as the starting input for the series from the direction of the input, and the second layer serving as the starting input from the direction of the output.
To splice and combine the backward-conducted information vector
5.3 Step-by-step prediction of multiple loads
The prediction priority of the multiple loads in the IES can be determined based on the cumulative correlation coefficient
On the basis of the load prediction method of the above priority, the idea of prioritized step-by-step prediction is adopted, as the electrical and cold loads have the weakest correlation for heat loads, followed by electrical and heat loads for cold loads, and again by heat and cold loads for electrical loads. Accordingly, the predicted heat load can be considered first, and the predicted heat load is first used as input combined with the NWP characteristics of the strong correlation of the cold load and the load mean value series of the first 16 time points of the prediction period for the prediction of the cold load in the set of sub-load sc. Secondly, the predicted hot load and cold load are used as the input combined with the strong correlation NWP characteristics of the electric load and the load mean value series of the first 16 time points of the prediction period to predict the electric load. Finally the overall prediction results of the multiple loads are obtained, where the overall prediction process is shown in Figure 7.
6 Example analysis
6.1 Data source
Example load data from Campus Metabolism for the Temp campus of Arizona State University from 1 January 2019 at 00:00 h to 31 December 2019 at 24:00 h for electricity, heat and cold (Arizona State University, 2022), Meteorological data were obtained from the National Climatic Resources website for the Tempe campus location (U.S. Department of Energy, 2022), including temperature, relative humidity, wind speed, wind direction, irradiance, rainfall availability, dew point, atmospheric pressure, and cloud type, at 15-min sampling intervals.
6.2 Model evaluation metrics
In this paper, Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) are selected as the evaluation indexes, and the expressions of the specific evaluation indexes are as follows.
Where
6.3 Load scenario set division results
Based on the results of gray correlation degree analysis in this paper, the highest priority electric load is used as the basis for the division of load scenarios, and the similar fluctuating sets of load for 12 months of a year are obtained according to the similarity and fluctuation division of load sequences. Among them, the load of a year is divided into four sets by month as shown in Table 1.
The correlation degree analysis of the strongly correlated NWP features of the multivariate loads is required before the load scenario set division, based on the results of the gray correlation degree analysis, as shown in Table 2. The results show that the strength of the correlation between each meteorological influence factor and the electric, heat and cold loads varies, so it is necessary to correspond the meteorological influence factors to the load types in a reasonable way when prediction.
Based on the results of the strong correlation NWP fluctuation analysis of multiple loads, the NWP features of each load are input into the k-means clustering model to obtain the clustering cluster division of each load under each fluctuation set and train the BI-LSTM model separately for each cluster to realize the sub-load scenario set prediction.
6.4 Multiple load prediction priorities
Since the paper is mainly based on the coupling characteristics of multiple loads to prioritize the single load sequence for load prediction, the coupling relationship of multiple loads obtained based on gray correlation analysis is shown in Table 3. The influence of day type information and meteorological influences on the degree of load coupling is taken into account. For the first predicted heat load, six meteorological factors with the highest correlation are selected for prediction, and then the cold load is predicted iteratively using the predicted heat load data and the corresponding meteorological influences on the cold load. And so on, ensuring that each load forecast from the second priority load forecast onwards iterates over the previous load forecast results and the newly added meteorological impact factors. The following results can be obtained from Table 3:
6.5 Analysis of experimental results
6.5.1 Prediction results for load scenario set partitioning
According to the idea of differentiated priority prediction, the heat load is first predicted. Prediction method 1: A sequence of meteorological factors strongly correlated with the heat load and a sequence of heat load history data are used as inputs, where the input meteorological factors are forecast data for the forecast period and the heat load history data are measured data for the 4 hours before the forecast period. Prediction method 2: Extract the strongly correlated meteorological element sequences within the similar fluctuation set with time interval of 1 hour by day as the input of k-means clustering model for load scenario set division, and train the model separately for prediction by load scenario set according to the division result, at which time the input is the same as prediction method 1. In order to highlight the effect of clustering division, 2 days per month are selected for load prediction for similar fluctuation concentration at this time, and the load prediction results for 6 days are obtained, as shown in Figure 8, which shows that the prediction curve of the proposed prediction method in this paper is closer to the actual value curve.
Among them, the error evaluation indexes of the prediction results are shown in Table 4. The errors obtained by prediction method two based on load scenario set division are smaller compared with those obtained by prediction method 1, in which MAPE is reduced by 1.59% on average, RMSE is reduced by 0.14 MMBtu·h−1 on average, and MAE is reduced by 0.11 MMBtu·h−1 on average.
6.5.2 Prediction results for load scenario set partitioning
Prediction results base on heat load, the coupling characteristics are considered for the prediction of cold load taking into account the priority of multiple load prediction. In this case, due to the consideration of the coupling characteristics, the prediction results of the heat load, as well as the strongly correlated meteorological characteristic series and the actual cold load series of the first 4 hours of the prediction period are used as inputs. The prediction method 1 that directly predicts considering the coupling characteristics, the prediction method 2 that divides the set of load scenes without considering the coupling characteristics, and the prediction method 3 that considers both the coupling characteristics and the division of the set of load scenes are obtained. The prediction results are shown in Figure 9.
A comparison of the results of multiple prediction methods is shown in Table 5, where the prediction method 2 without considering the coupling characteristics is less effective, with the highest average absolute percentage error reaching 10.26%, prediction method 3 has the smallest prediction error because it considers the coupling characteristics of multiple loads and divides the set of load scenarios. Compared with the prediction method 1, the MAPE was reduced by 2.06%, RMSE by 278.93 Ton, and MAE by 188.82 Ton on average, compared to prediction Method 2, MAPE is reduced by 2.98% on average, RMSE by 219.30 Ton on average, and MAE by 389.47 Ton on average.
According to the above obtained heat and cold load prediction values, the electric load is predicted by considering the coupling characteristics. In this case, the electrical load forecast inputs include the forecasted hot and cold loads as well as strongly correlated meteorological characteristics and measured electrical load data for the first 4 hours of the forecast period. As the coupling characteristics and load scenario set division are considered, the same three prediction modes as the cold load are obtained, and the prediction results are shown in Figure 10. It can be seen that the average absolute percentage error of prediction method 3 decreases by 0.55% and 0.66% on average compared with prediction method 1 and prediction method 2. Respectively, which is due to the fact that prediction method 3 takes into account the characteristics of multi-energy coupling in IES and uses the remaining two loads as references, which in turn improves its prediction accuracy.
Due to the consideration of multivariate load coupling characteristics and the division method of load scenario set, the prediction results of prediction method 3 have less prediction error than the other two prediction methods, This is mainly reflected in the peak and valley hours of the load, and the prediction method 3 is more reflective of the actual load variation, which reflects that the prediction method considering the division of load scenarios and taking into account the coupling characteristics is more practical for IES multi load prediction. The electrical load forecast errors are shown in Table 6. Although the electric load prediction errors obtained by the three prediction methods are not significantly different, the prediction errors obtained by the prediction method considering coupling characteristics and load scenario set division are smaller.
7 Conclusion
1) In order to improve the accuracy of multivariate load prediction, this paper proposes a similar fluctuation feature set partitioning method considering the coupling characteristics of multivariate loads. By studying the coupling strength between multiple types of energy in IES to determine the prediction priority of multiple loads, the interactive coupling between electric, heat and cold loads is fully considered. The NWP features with strong correlation with multiple loads are obtained by gray correlation analysis, and k-means clustering is performed using the inter-set NWP as input, and fine-grained modeling is performed for each cluster. The analysis of the arithmetic examples leads to the following main conclusions.
2) Based on the load similar fluctuation set division method proposed in this paper, four fluctuation sets are obtained. The load fluctuation types within each fluctuation set are similar, and the load scenario set is divided based on the k-means clustering model for each load similar fluctuation set. And according to the results of the above analysis, the priority of load forecasting is considered, and the heat load, cold load and electric load are predicted step by step according to the fluctuation set and load scenario set respectively.
3) The prediction results based on multiple prediction methods show that the average absolute percentage errors of electric, heat, and cold loads are 2.85%, 5.22%, and 4.06% for the prediction methods considering the division of load scenario sets and coupling characteristics in the overall prediction results of four load similar fluctuation sets. The average absolute percentage error is reduced by 0.66%, 1.53% and 2.99% respectively compared to the prediction method considering only load scenarios, compared with the prediction method without considering load scenes, its average absolute percentage errors are reduced by 0.55%, 1.53%, and 2.07%, respectively, which verifies the effectiveness of the prediction method considering the division of load scenes set and coupling characteristics.
With the popularity of IES and the development of interconnected IES, the division of IES types can be considered in the subsequent study so as to realize the collaborative prediction among multiple IESs.
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Author contributions
MY: Writing—original draft. DL: Validation, Visualization. XS: Methodology. JW: Software, Supervision. YC: Data curation.
Funding
This work was supported by National Key R&D Program of China [Technology and application of wind power/photovoltaic power prediction for promoting renewable energy consumption (2018YFB0904200)].
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
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References
Abhishek, S., and Sachin, K. J. (2022). A novel seasonal segmentation approach for day-ahead load forecasting. Energy 257, 124752. doi:10.1016/j.energy.2022.124752
Arizona State University (2022). Campus metabolism. https://cm.asu.edu (Accessed August 11, 2022).
Fulong, Y., Wenju, Z., Mostafa, A. G., Yang, S., and Wanqing, Z. (2022). An integrated D-CNN-LSTM approach for short-term heat demand prediction in district heating systems. Energy Rep. 8 (13), 98–107. doi:10.1016/j.egyr.2022.08.087
Haohan, T., Zhisheng, Z., and Daolin, Y. (2021). Research on multi-load short-term forecasting of regional integrated energy system based on improved LSTM. Proc. CSU-EPSA. 33 (9), 130–137. doi:10.19635/j.cnki.csu-epsa.000726
Hongbo, R., Qifen, L., Qiong, W., Chunyan, Z., Zhenlan, D., and Jie, C. (2022). Joint forecasting of multi-energy loads for a University based on copula theory and improved LSTM network. Energy Rep. 8 (10), 605–612. doi:10.1016/j.egyr.2022.05.208
Jinpeng, C., Zhijian, H., and Weinan, C. (2021). Load prediction of integrated energy system based on combination of quadratic modal decomposition and deep bidirectional long short-term memory and multiple linear regression. Autom. Electr. Power Syst. 45 (13), 85–94. 10. 7500/AEPS20200829004.
Jixiang, L., Qipei, Z., Zhihong, Y., Mengfu, T., Jinjun, L., and Hui, P. (2019). Short-term load forecasting method based on CNN-LSTM hybrid neural network model. Autom. Electr. Power Syst. 43 (08), 131–137. doi:10.7500/AEPS20181012004
Jizhong, Z., Hanjiang, D., Shenglin, L., Ziyu, C., and Tengyan, L. (2021). Review of data-driven load forecasting for integrated energy system. Proc. CSEE 41 (23), 7905–7924. doi:10.13334/j.0258-8013.pcsee.202337
Jizhong, Z., Hanjiang, D., Weiye, Z., Shenglin, L., Yanting, H., and Lei, X. (2022). Review and prospect of data-driven techniques for load forecasting in integrated energy systems. Appl. Energy 321, 119269. doi:10.1016/j.apenergy.2022.119269
Kai, Z., Jian, F., Jianhua, L., Xinlei, B., Feixiang, G., Zudong, L., et al. (2022). Power consumption behavior pattern classification based on fuzzy C⁃mean clustering algorithm. Power Demand Side Manag. 24 (3), 98–103. doi:10.3969/j.issn.1009-1831.2022.03.016
Luiz, F., and Afshin, A. (2015). Short-term forecasting of the abu dhabi electricity load using multiple weather variables. Energy Procedia 75, 3014–3026. doi:10.1016/j.egypro.2015.07.616
Mao, Y., Chaoyu, S., and Huiyu, L. (2020). Day-ahead wind power forecasting based on the clustering of equivalent power curves. Energy 218, 119515. doi:10.1016/j.energy.2020.119515
Mao, Y., Meng, Z., Dawei, H., and Xin, S. (2022). A composite framework for photovoltaic day-ahead power prediction based on dual clustering of dynamic time warping distance and deep autoencoder. Renew. Energy 194, 659–673. doi:10.1016/j.renene.2022.05.141
Mukhopadhyay, P., Mitra, G., Banerjee, S., and Mukherjee, G. (December 2017). “Electricity load forecasting using fuzzy logic: Short term load forecasting weather parameter,” in Proccedings of the 2017 7th international conference on power systems (ICPS), Pune, India 812–819. doi:10.1109/ICPES.2017.8387401
Nantian, H., Qingkui, H., Jiajin, Q., Qiankun, H., Rijun, W., Guowei, C., et al. (2022). Multinodes interval electric vehicle day-ahead charging load forecasting based on joint adversarial generation. Int. J. Electr. Power & Energy Syst. 143, 108404. doi:10.1016/j.ijepes.2022.108404
Qingkai, S., Xiaojun, W., Yizhi, Z., Fang, Z., Pei, Z., and Wenzhong, G. (2021). Multiple load prediction of integrated energy system based on long short-term memory and multi-task learning. Autom. Electr. Power Syst. 45 (5), 63–70.doi:10.7500/AEPS20200306002
Tang, Y., Liu, H., Xie, Y., Zhai, J., and Wu, X. (2019). Short-term forecasting of electricity and gas demand in multi-energy system based on RBF-NN model. IEEE Int. Conf. Energy Internet (ICEI), 542–547. doi:10.1109/ICEI.2019.00102
U.S. Department of Energy (2022). NSEDB:National solar radiation database. https://nsrdb.nrel.gov/ (Accessed August 11, 2022).
Valery, S., Evgeny, B., Dmitry, S., and Bin, Z. (2022). Current state of research on the energy management and expansion planning of integrated energy systems. Energy Rep. 8, 10025–10036. doi:10.1016/j.egyr.2022.07.172
Wang, X., Wang, S., Zhao, Q., Wang, S., and Fu, L. (2021). A multi-energy load prediction model based on deep multi-task learning and ensemble approach for regional integrated energy systems. Int. J. Electr. Power & Energy Syst. 126 (A), 106583. doi:10.1016/j.ijepes.2020.106583
Wei, W., Lin, X., Jierui, X., Chang, L., Xiaofeng, J., and Kai, L. (2022). Coupled dispatching of regional integrated energy system under an electric-traffic environment considering user equilibrium theory. Energy Rep. 8, 8939–8952. doi:10.1016/j.egyr.2022.07.008
Wu, Y., Liang, X., Huang, T., Lin, Z., Li, Z., and Hossain, M. (2021a). A hierarchical framework for renewable energy sources consumption promotion among microgrids through two-layer electricity prices. Renew. Sustain. Energy Rev. 145, 111140. doi:10.1016/j.rser.2021.111140
Wu, Y., Lin, Z., Liu, C., Chen, Y., and Uddin, N. (2021b). A demand response trade model considering cost and benefit allocation game and hydrogen to electricity conversion. IEEE Trans. Ind. Appl. 58, 2909–2920. doi:10.1109/TIA.2021.3088769
Xiaobo, Z., and Jianzhou, W. (2018). A novel decomposition-ensemble model for forecasting short-term load-time series with multiple seasonal patterns. Appl. Soft Comput. 65, 478–494. doi:10.1016/j.asoc.2018.01.017
Xuexiang, L., Haowen, L., Xudong, Z., Zhonghe, H., Yu, C., and Min, Y. (2022). A novel neural network and grey correlation analysis method for computation of the heat transfer limit of a loop heat pipe (LHP). Energy 259, 124830. doi:10.1016/j.energy.2022.124830
Yang, M., Zhang, L., Cui, Y., Zhou, Y., Chen, Y., and Yan, G. (2019). Investigating the wind power smoothing effect using set pair analysis. IEEE Trans. Sustain. Energy 11, 1161–1172. doi:10.1109/TSTE.2019.2920255
Keywords: load prediction, scenario set partitioning, Bi-LSTM, integrated energy system, meteorological factors
Citation: Yang M, Liu D, Su X, Wang J and Cui Y (2023) Ultra-short-term load prediction of integrated energy system based on load similar fluctuation set classification. Front. Energy Res. 10:1037874. doi: 10.3389/fenrg.2022.1037874
Received: 06 September 2022; Accepted: 31 October 2022;
Published: 16 January 2023.
Edited by:
Yingjun Wu, Hohai University, ChinaReviewed by:
Shuaishi Liu, Changchun University of Technology, ChinaXiaomeng Ai, Huazhong University of Science and Technology, China
Copyright © 2023 Yang, Liu, Su, Wang and Cui. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Dongxu Liu, 458162373@qq.com