1. Introduction
Traditional electric service mechanisms are undergoing rapid and continuous changes with the increasing penetration of economical, reliable, and environmentally friendly microgrids [
1,
2]. A microgrid consists of distributed generation devices, such as wind turbines (WTs) and photovoltaics (PVs), an energy storage system (ESS), and controllable loads. It can efficiently manage generation and loads and operate in the grid-connected and islanding mode, enabling it to exchange energy between a main-grid and neighboring microgrids. As seen from
Figure 1, microgrids are a global phenomenon. Different regions around the world are investing into microgrids, expecting huge increments in revenue.
Renewable energies play a major role in the energy sector, primarily in microgrids, due to the ability to combat global warming and provide a more economical and diversified energy mix, ensuring energy security and sustainability, as shown in
Figure 2. Despite comprehensive increases in the size and installed capacity, the uncertainty and variability of renewable energy generation pose big challenges. Additionally, to help the grid’s operation with planning, maintenance, and operation, energy quantities should be forecasted [
3,
4]. Concomitant benefits can be obtained by an accurate long-term renewable generation forecast, firstly, to help to carry out planning and maintenance, secondly, to minimize penalties and charges due to the imbalance of generated power, and thirdly, to provide good knowledge of future energy market trading [
5,
6]. As shown in
Figure 2, solar generation is one of the most common types of renewable energy that has grown rapidly over the past decade, and it is expected to grow even faster in the future [
3,
7,
8,
9].
Very long-term solar power generation forecasting is essential for engineering and planning of microgrid installation [
10]. It is necessary to estimate renewable generation capacity, energy storage system (ESS) capacity, total demand, simulation capacities, and microgrid market participation [
10]. Numerous parameters affect solar generation forecasting, but solar radiation is the key component for solar generation [
11]. Therefore, at least one year ahead long-term solar radiation needs to be forecasted correctly. Very long-term solar radiation forecasting is also required for estimating the degradation-rate-influenced energy potentials of PV-panels. Three year ahead forecasting of solar radiation is done in [
12] in order to estimate the degradation-rate-influenced energy potentials of a thin amorphous silicon (a-Si) PV system.
Various approaches are adopted to forecast solar radiation using historical data, numerical weather data, cloud image from satellites, etc. [
13,
14,
15,
16,
17,
18,
19]. Probabilistic radiation forecasting was built based on the non-parametric approach [
20] and calculated prediction intervals using a k-nearest neighbor’s regression model. In [
21,
22], probabilistic solar radiation forecasting was generated using an analogue ensemble method. Spatial and temporal day ahead total daily radiation forecasting using ensemble forecasting based on the empirical biasing was proposed in [
23]. In [
24], Lasso was used to perform a 5 min radiation forecasting. In [
25], k-nearest neighbor and support vector machines were used to identify the impact of weather classification on solar radiation data. Hourly solar radiation forecasting using a Volterra-least squares support vector machine model combined with signal decomposition was done in [
26]. Deep learning is gaining huge success in many fields. Deep learning was used for forecasting solar radiation using a six-month UTSA SkyImager dataset in [
27]. Day-ahead solar radiation forecasting for microgrids uses long short term memory (LSTM) as a deep learning model [
28]. Hour ahead solar radiation is forecasted using gated recurrent units (GRUs) [
29].
All the methods above are for short term forecasting ranging from hours to days. To the best of the authors’ knowledge, several attempts were made for one year ahead demand forecasting using time series methods and deep learning methods [
30,
31]. However, only two attempts were made to predict solar radiation for one year ahead [
10,
12]. In the probabilistic methods, the clearness index, which is the most influential parameter for solar radiation, is calculated using the probabilistic approach, so there are possibilities for error in the process of probability calculation [
10]. The paper [
12], as a micro-article short of detailed description, was based on machine learning but used only historical data. Moreover, the procedure of dealing with the data was not clearly explained in [
12]. Solar radiation forecasting is a time series problem. The next time step output is dependent on the current time step and past inputs. Deep learning has succeeded quite remarkably in dealing with time series data [
32].
In this paper, for the first time, different deep learning models are used for one year ahead hourly and daily period solar radiation forecasting. The proposed method is a novel approach in terms of data management and the application of deep learning approaches for one year ahead solar radiation forecasting. This method uses historical solar radiation data and clear sky global horizontal irradiance (GHI). Different clear sky GHI models are compared with respect to the problem, hence, selecting the most appropriate clear sky model. A comparison of GRU, LSTM, recurrent neural network (RNN), feed forward neural network (FFNN), and support vector regression (SVR) are made to check the effectiveness of each model. Random forest regression (RFR) was considered as an efficient method for solar radiation forecast in [
12]. Therefore, the proposed method is also compared with RFR. The paper is organized as follows:
Section 2 explains the data selection and management. This includes input data selection and how these data are used to achieve the objective. In
Section 3, deep learning architectures used in this paper are explained, while
Section 4 explains the experimental setup and results.
Section 5 gives the discussion about the results.
Section 6 gives the conclusion.
4. Experiment and Results
Real-time hourly and daily solar radiation data was obtained from the Korea Meteorological Administration (KMA) for Seoul and Busan regions in South Korea. The two regions were selected considering their geographical differences. As shown in
Figure 10, Seoul is in the northern part of Korea surrounded by mountains while Busan is located at the southern coastline of the country.
As discussed in the data selection section, for both regions, historical hourly and daily solar radiation data from 2000 to 2016 were used to train the model along with clear sky GHI data, while 2017 hourly and daily solar radiation were predicted, respectively. Different deep learning models were implemented and compared to achieve this objective. The models compared were the state of the art models: SVR, RNN, FFNN, LSTM, and GRU. Comparison of the proposed models with the state of the art traditional method, i.e., RFR was also made. The models were implemented in Python using a Jupyter Notebook with Keras and TensorFlow at the back-end. The error criteria used in this paper was root mean square error (RMSE).
Results
Table 2 and
Table 3 show the comparison of RMSE for hourly and daily periods. Training data from 2000 until 2015 and corresponding clear sky GHI data were used to predict data from 2016. Similarly, data from 2000 to 2016 with corresponding clear sky GHI data were used to predict 2017 data.
Table 4 and
Table 5 show performance of LSTM and GRU models. The performance was measured as training time in a system with an AMD Ryzen Threadripper 2950X and 64 GB RAM, and only a CPU was used for model fitting and prediction. The measurements were mean values taken from 10 runs for accurate results.
Table 6 compares total radiation of each model for one year ahead.
Figure 11 shows absolute values of prediction errors for each model in the perspective of total yearly radiation. The lower value indicates that it is more similar to the actual data.
Figure 12 and
Figure 13 show comparisons of actual data vs. standard RNN and its extensions (i.e., LSTM, GRU) trained with the training data until year 2016, for the prediction of year 2017 in monthly time steps for two regions, respectively.
6. Conclusions
For microgrid design and engineering, it is necessary to estimate renewable generation capacity, energy storage system (ESS) capacity, total demand, simulation capacities, etc. For these purposes, very long-term generation and demand need to be forecasted. Solar power generation forecasting mainly depends on the amount of solar radiation. Therefore, long-term solar radiation is required to be forecasted. Long-term solar radiation forecasting is also necessary for the estimation of the degradation-rate-influenced energy potentials of PV panels. Traditionally probabilistic approaches are used for long-term solar radiation forecasting. However, due to uncertainty from probability based randomness, these approaches are less accurate. In previous works, machine learning algorithms like RFR were also being used. In this work, we applied the deep learning-based approach to predict long term solar radiation due to its huge success in diverse fields including time series forecasting.
Historical solar radiation data and clear sky GHI data predicted from the most suitable clear sky GHI model were used as input data. Different state of the art machine learning (ML) and deep learning (DL) approaches were applied and compared. The models compared were traditional methods like RFR and SVR and state of the art deep learning models such as FFNN, RNN, LSTM, and GRU. The deep learning models outperformed the traditional methods in terms of model accuracy. Among the deep learning models, LSTM and GRU were better than others due to their characteristics of carrying important information over a long distance. Among two of them, GRU showed slightly better results compared to LSTM. In addition to model accuracy, the performance of two state of the art deep learning models, LSTM and GRU, was also measured. Among them, GRU performed relatively faster due to its fewer gates than LSTM. From these observations, we proposed LSTM and GRU as promising deep learning-based approaches for long-term solar radiation forecasting.
With respect to long-term solar radiation forecasting, since our proposed approach reached a new state of the art in terms of accuracy, we expect our work can be applied to different kind of applications. Precisely, predicted one year ahead hourly and daily data can be used for the following purposes: Firstly, as discussed above, it can be used for simulation design, installation, and planning of renewables, especially in microgrids. Secondly, this yearly ahead data can be used to study the degradation-rate-influenced energy potentials of PV panels. Thirdly, meteorological departments can take help from predicted data to carry out weather-related research. Additionally, it has been found that GRU is more suitable for performance-critical environments like IEDs in smart grids since GRU has the smallest calculation cost.