1. Introduction
As the energy crisis intensifies, renewable energy sources such as wind and solar energy as have been widely concerned. According to statistics, the world’s wind power generation in 2020 reached 733 GW which increased by 17.8% over 2019. The world’s solar power generation in 2020 reached 714 GW and increased by 21.6% over last year [
1]. In fact, both wind and photovoltaic (PV) power generation are fluctuant and intermittent. High renewable energy penetrated power systems bring severe challenges to security and economy of the power grid [
2]. As a result, under the premise of ensuring the economic and stable operation of the power grid, we should improve the ability of the power grid to penetrate more renewable energy.
In order to meet this requirement, on the one hand, energy storage devices can be installed on the grid side, such as concentrating solar power (CSP) with heat storage [
3,
4]. On the other hand, according to the complementary and smoothness of wind and solar energy [
5,
6], it is also an effective means to raise the renewable energy penetration by bundling multiple types of renewable energy in the same region [
7]. Nevertheless, CSP and bundling grid connection methods cannot guarantee the stability of renewable energy generation. Moreover, CSP, wind power and PV power are intermittent, random, and difficult to predict accurately, which lead to more complex uncertainty of power generation cluster and more obvious time and space complementary of power generation. In the context of large scale and multi-types renewable energy clusters, it is necessary to explore the change regularities of regional renewable generation and forms a power forecasting method which is suitable for wind power, PV power and CSP cluster.
With the improvement of computer operation speed, more machine learning and artificial intelligence approaches have been used in forecasting. Normally, compared with the statistical and physical forecasting approaches, machine learning approaches can get better results [
8,
9,
10]. However, machine learning needs a large amount of data in the training process, and the model with large fluctuation of training data is easy to over fit [
11,
12,
13]. Ahmed et al. [
14] summarize the recent photovoltaic power forecasting (PVF) methods including physical, statistical, artificial intelligence, ensemble and hybrid approaches, the results show that the ensembles of artificial neural network (ANN) is most suitable for short-term PVF. Ding [
15] summarizes and compares several common forecasting methods of wind power generation (WPG) such as the autoregressive integrated moving average (ARIMA) model, Kalman filter (KF), support vector machine (SVM) and ANN. The main advantage of ANN is its ability to study and deal with nonlinearity in WPG data. In order to find a more suitable power generation forecasting method, Sharifzadeh et al. [
16] compared ANN, SVM and Gaussian process regression. All these models can forecast wind and solar power, but only the ANN can successfully consider the electricity demand. Although ANN can analyze time series data, when the time series is too long, the forecast accuracy is low. In order to solve this problem, a long short-term memory (LSTM) based on gating unit is proposed [
17]. Because of its unique structural characteristics, LSTM has great advantages in processing long time series such as renewable energy generation data. Considering that LSTM can extract and learn the correlation in hours of solar data and even longer term information, Wang et al. [
18] proposed a hybrid forecasting strategy which combines LSTM with Gaussian process regression. The results show that this method is better than SVM and other methods in WPG forecasting. Besides, renewable energy may have great changes in different seasons and weather conditions. When the data set is insufficient, the change of weather and season is often ignored in renewable energy forecasting [
9]. For example, if the model is trained with the data in summer, the forecasting performance of the model may be better in summer, but it is not sure that the model has the same performance in winter [
19]. That’s why we should consider the applicability of the LSTM model. Nevertheless, these methods independently forecast one type of power generation such as PV or WPG. The authors in [
20] propose that ANN can be used to forecast hybrid renewable energy systems. By comparison, LSTM has better performance than multi-layer perceptron (MLP) and other simple ANN in forecasting of hybrid renewable energy systems.
In traditional regional prediction models, most of them are renewable energy generation predictions of the same power generation type [
21]. The combined prediction of multiple units greatly improves the accuracy of the prediction model [
22]. Consider the application scenarios of renewable energy clusters with multiple power generation types are common in these days [
23]. The wind power and PV power generation can be predicted separately, and then the regional power can be obtained by simple accumulation. Because of the complementary between solar and wind energy, when wind turbines and photovoltaic power plants are built in a power plant or region, the combined use of wind and solar data can improve the accuracy of prediction [
24,
25]. When there is CSP in the area, there is a strong trend correlation between CSP and PV power generation [
26]. Due to the flexibility of CSP power generation, the uncertainty of photovoltaic power generation and wind power can be weakened to a certain extent [
27,
28]. With this just the opposite is, the nonlinear relationship between CSP generation and the other two types of renewable energy power generation is more complicated. For renewable energy clusters including CSP generation, the applicability of typical statistical method and cumulative method cannot be guaranteed.
In view of the above concerns, this paper proposes a short-term hybrid prediction model based on LSTM neural network. In this way, the spatial and temporal complementary characteristics of different units are deeply explored. The advantages and disadvantages of bundling prediction of various types of renewable energy are compared with the traditional independent prediction. Finally, a short-term hybrid prediction model based on LSTM is established. The research work shows that the method can effectively describe the complex uncertainty of the large-scale and multiple types renewable energy, which has great advantages in renewable energy cluster forecasting. The rest of this paper is summarized as follows: The second part introduces the renewable power generation cluster including CSP generation and the structure of LSTM neural network. Part III describes the construction process of short-term hybrid prediction model based on LSTM neural network. In the fourth part, through the measured data of a certain region in China, the method proposed in this paper is compared with traditional independent prediction method. Finally, the main contributions are summarized in Part V.
4. Experimental Results and Analysis
4.1. Case Setup
In addition to theoretical analysis, the effectiveness of the method needs to be verified by cases.
Section 3.2 analyzes the correlation among the three types of power generation, and it should be proved by an example that the forecasting efficiency can be significantly improved by hybrid forecasting among the generation types with correlation. On this basis, Wind-PV-CSP hybrid generation bundled into the power system are superior is verified by the evaluation of hybrid forecasting, partial hybrid forecasting and independent forecasting. Finally, the correctness of
Section 2 theory should be proved by comparative experiments. In the hybrid forecasting of multiple renewable energy sources, LSTM network do better than the traditional RNN network.
- (1)
Case 1
In case 1, a hybrid forecasting scheme for CSP and PV power generation is modeled. As a contrast, a scheme for independent forecasting of CSP and PV power generation is established. The main purpose of case 1 is to prove that the hybrid forecasting between PV and CSP with significant correlation can effectively improve the forecasting accuracy. For hybrid forecasting, the power of regional PV and CSP can be obtained directly. The difference is that for independent forecasting, the sum of CSP and PV generation in the region can be obtained by accumulation. The input data of these two models include historical power data and meteorological data (MD). The meteorological data include wind speed and DNI. We record more detailed information about each scheme in
Table 4. The models of these two forecasting schemes are shown in
Figure 5.
Although PV was significantly correlated with CSP, WPG had a weak correlation with CSP and PV. In order to prove whether the hybrid forecasting between generation types with weak correlation can improve the forecasting accuracy, we established the hybrid forecasting of WPG and PV and the hybrid forecasting of WPG and CSP. The details are recorded in
Table 4.
- (2)
Case 2
Based on correlation analysis, hybrid generation bundled into the power system are superior is verified by the evaluation of hybrid forecasting, partial hybrid forecasting and independent forecasting. A total of five experimental groups were set up and recorded in
Table 5. Independent forecast details are recorded in group 1. Details of the partial hybrid forecasts are recorded in groups 2, 3, and 4. Details of the hybrid forecasting are recorded in group 5.
The differences of the above five prediction models in structure can be expressed more intuitively by mathematics. The mathematical expressions of group 1 to group 5 are expressed by Equations (8)–(12) respectively:
where:
P∑ is the total output of the new energy cluster at
t + 1 time,
V is the row vector of historical wind speed,
D is the row vector of historical DNI,
PWPG is the historical power data of wind power plant,
PPV is the historical power data of PV power plant, and
PCSP is the historical power data of CSP plant.
The length of time series input into LSTM is 24, then two sets of historical meteorological data and three sets of historical output data are expressed by Equations (13)–(17):
- (3)
Case 3
In case 3, it is verified that the hybrid forecasting model based on a LSTM neural network is superior to other forecasting algorithms. It has been proved that LSTM can extract more effective information from historical data which could be used to forecast future power, the LSTM with long-term memory can be used to analyze the data with obvious time lag. Although
Section 2.2 analyze the theory through LSTM models. But in reality, the model becomes extremely complex. We hope to prove the correctness of this theory through the experiment of real data. The specific arrangement of case 3 is shown in
Table 6.
By changing the length of time series, it is proved that the “long-term memory” ability of LSTM network can effectively improve the forecasting accuracy of hybrid forecasting model. The length of time series is 24 h, 12 h and 3 h respectively. Besides, the other three kinds of forecasting model are also used in this hybrid forecasting method, they are ARIMA, MLP neural network and RNN. Their forecasting performance is compared with LSTM network.
4.2. Result Analysis
Figure 6 shows the actual and forecast power of PV and CSP in December. The RMSE% of hybrid and independent forecasting are 7.09% and 6.46%, respectively. It is obvious that the power of PV and CSP can be forecast by the hybrid forecasting method, which gives better results. It is clear that the result is unconvincing, which because of the same results cannot be guaranteed out of the sample. Twelve-fold cross validation is used to prove the generality and universality of the model. The monthly RMSE obtained by cross validation is recorded in
Figure 7. Compared with the forecasting results in December, the hybrid forecasting model based on LSTM still has good forecasting in the other 11 groups of samples. Similar to the hybrid forecasting of PV and CSP, the other two groups have the same process. In order to record the forecast results more clearly, the average RMSE and RMES% obtained by cross validation are recorded in
Table 7.
For any two renewable energy sources among WPG, PV and CSP, the RMSE% of hybrid forecasting is lower than RMSE% of independent forecasting. The simulation results show that the hybrid forecasting between the two types of power generation with correlation can greatly improve the forecasting accuracy.
- 2
Case 2
In Case 1, we know that any two combinations of PV, CSP, WPG is better than independent forecasting. We are still curious whether the hybrid forecasting among PV, CSP and WPG can improve the forecasting accuracy. In this case, we have done five comparative groups to test that theory. The groups of case 2 are shown in
Table 5, where group 1 is an independent forecasting, groups 2 to 4 are partial hybrid forecasting, and group5 are hybrid forecasting. Their results were added together and the statistical indicators were recorded in
Table 8.
Figure 8 records the forecast results of five prediction schemes on a day in December. For better comparison, we recorded the annual average of RMES and RMSE% in descending order in
Table 8 and
Figure 9. Obviously, the RMSE% of independent forecasting is less than that of partial hybrid forecasting, and the RMSE% of hybrid forecasting model is the smallest. Finally, we believe that hybrid forecasting among PV, CSP and WPG can improve the forecasting accuracy. The effect of hybrid forecasting is best because of the following reasons.
First of all, because wind speed and DNI are correlated meteorological variables, there is a weak correlation between WPG and PV. During the day, the DNI is high and the wind speed is often weak. After the sun sets at night, the wind speed is strengthened because of the great change of the surface temperature difference. Besides, the wind speed is often higher on rainy days than on sunny days. What is more, In China’s monsoon climate region, generally, wind speed and DNI are complementary in season. In short, it is precisely because of these meteorological correlations that WPG and PV power generation show a certain correlation. Besides, DNI is the main meteorological variable determining PV and CSP power generation, so there is a trend correlation between PV and CSP. The correlation is very strong when there is no power compensation. What is more, in the hybrid power generation system, CSP is used to compensate for the fluctuation of PV and WPG generation, so there is a certain negative correlation between the generation of CSP and the other two types of generation. Finally, the three types of power generation may be affected by temperature, air pressure, humidity and other meteorological factors at the same time. There is a certain coupling relationship among these types of power generation.
- 3
Case 3
The Wind-PV-CSP hybrid forecasting was repeated by changing the time series length of LSTM neural network. The forecasting results show that with the shortening of time series, the value of RMSE% becomes larger and the forecasting accuracy becomes worse, which because the important information is lost by LSTM. The RMSE% of the LSTM forecasting model under different time series lengths are recorded in
Table 9 and
Figure 10.
In addition to changing the time series length of LSTM neural network, the hybrid forecasting model of Wind-PV-CSP by other three forecasting model are also modeled, and the RMSE% of these three forecasting models are recorded in
Table 9 and
Figure 10. The results show that the LSTM method with long-term memory has a higher forecasting accuracy in the hybrid forecasting of Wind-PV-CSP generation.
5. Conclusions
High proportion of renewable energy clusters connected to the power grid are expected to be an important trend in the future. Improving the forecasting accuracy of these hybrid generation clusters is a great challenge and a difficult task. This paper proposes a short-term forecasting method based on LSTM for hybrid generation cluster composed of three types of renewable energy power generation, namely wind power, photovoltaic power and CSP. A series of conclusions can be drawn from the results of this study.
The generation cluster of CSP and photovoltaic power generation can reduce the uncertainty of renewable energy generation, so that the hybrid forecasting of CSP and PV generation has higher forecast accuracy than the independent forecasting. Besides, complementarity between wind and solar energy is also used for the forecasting of hybrid generation cluster. The hybrid forecasting improves the forecast accuracy of Wind-PV-CSP generation cluster. Additionally, LSTM can make better use of the correlation and long time series data among different types of power generation. The memory function makes LSTM perform better in hybrid forecasting than that of ARIMA, MLP and RNN.
For future works, on the one hand, the structure of the hybrid forecasting is relatively simple and we are considering the ensemble method based on LSTM. On the other hand, for renewable energy cluster, only one group of PV, wind power and CSP data is used. The next step is increasing the number of power plants for each type of power generation.