Solar energy has gained attention in the past two decades, since it is an effective renewable energy source that causes no harm to the environment. Solar Irradiation Prediction (SIP) is essential to plan, schedule, and manage photovoltaic power plants and grid-based power generation systems. Numerous models have been proposed for SIP in the literature while such studies demand huge volumes of weather data about the target location for a lengthy period of time. In this scenario, commonly available Artificial Intelligence (AI) technique can be trained over past values of irradiance as well as weather-related parameters such as temperature, humidity, wind speed, pressure, and precipitation. Therefore, in current study, the authors aimed at developing a solar irradiance prediction model by integrating big data analytics with AI models (BDAAI- SIP) using weather forecasting data. In order to perform long-term collection of weather data, Hadoop MapReduce tool is employed. The proposed solar irradiance prediction model operates on different stages. Primarily, data preprocessing take place using various sub processes such as data conversion, missing value replacement, and data normalization. Besides, Elman Neural Network (ENN), a type of feedforward neural network is also applied for predictive analysis. It is divided into input layer, hidden layer, load-bearing layer, and output layer. To overcome the insufficiency of ENN in choosing the value of weights and hidden layer neuron count, Mayfly Optimization (MFO) algorithm is applied. In order to validate the performance of the proposed model, a series of experiments was conducted. The experimental values infer that the proposed model outperformed other methods used for comparison.
In general, sixty percent of a building's energy is consumed for ventilation, air-conditioning, and heating functions [
A combination of renewable sources, connected with an electric system, complicates the network management process and consistency of consumption or production balance, owing to its unpredictable and intermittent environment [
Artificial Intelligence (AI) technique has been applied in the recent years to predict performance improvement in SIP with regards to its capacity for simulating nonlinear and complex relations and manage the lost information [
Traditional methods like Multiple Linear Regression (MLR) and distinct kinds of AI techniques involving ANFIS have been established earlier to predict everyday global SR in Iraq by distinct metrological variables. The outcomes demonstrated that ANFIS offers precise outcomes than other prediction methods. A relative study conducted upon distinct AI methods in predictive SR exposed that ANFIS is one of the most appropriate methods for simulating SR. This is attributed to its capability to conquer the uncertainties related to time-sequential data. But, the main challenge of this method i.e., ANFIS is the change in hyper variables such as optimization of membership variable functions. Consequently, the research works conducted earlier combined classical ANFIS method with several optimization techniques to improve its efficiency. However, the efficiency of the present hybrid ANFIS method is too inspiring. However, its predictive ability needs improvement by assuming the significance of SR accuracy measurement. Moreover, one of the main drawbacks of present SR predictive method is its demand for several parameters as input. These parameters could not be made easily available due to lack of monitoring network.
The current study introduces an effective solar irradiance prediction model by integrating big data analytics and AI models (BDAAI- SIP) and weather forecast data is applied in this model. To manage the long-term collection of weather data, Hadoop MapReduce tool is utilized. At the beginning, the presented BDAAI-SIP model undergoes data preprocessing to boost the quality of weather-related data. Besides, Elman Neural Network (ENN), a type of Feedforward Neural Network (FFNN) is applied for predictive analysis. It can be separated as input layer, hidden layer, load-bearing layer, and output layer. To optimize the parameters, Mayfly Optimization (MFO) algorithm is used. In order to validate the efficacy of the proposed BDAAI-SIP model, a set of simulations was conducted. In short, the contributions of the paper are summarized herewith.
A novel BDAAI-SIP model is proposed to predict solar irradiation with the help of weather forecasting data. AI-based preprocessing is performed through three different ways such as data conversion, missing value replacement, and data normalization. ENN model, comprising of load-bearing layer, is employed for prediction purposes. In order to tune the weights and hidden layer neuron count in ENN model, MFO algorithm is applied. Parameter optimization of ENN model further helps in increasing the predictive results of the proposed BDAAI-SIP model The performance of BDAAI-SIP model was validated under several aspects and a comparative analysis was made.
Rest of the sections in this paper are organized as given herewith. Section 2 offers the existing works related to SIP. Section 3 introduces the system methodology of the proposed BDAAI-SIP model. Section 4 validates the performance of BDAAI-SIP model and Section 5 concludes the paper.
Several investigations have been conducted earlier with regards to Model Predictive Control (MPC), an optimal control approach that is introduced to assure effective system operation and control the air-conditioning process. Numerous researches have established the influence of decreasing the energy consumption of a building via MPC. The efficiency of MPC control is influenced by accurate information about hourly load prediction of a building. While this load consumption requirement gets influenced by climate data of the upcoming day. Thus, most of the methods require weather forecasting data. The general aspects that influence the loads are solar irradiance and outside air temperature. Though it is easy to predict outside air temperature due to small hourly variations, it is challenging to predict the real hourly values of solar irradiance.
In prior MPC investigations, solar irradiance prediction technique has been rarely stated. Several investigations in the literature utilized the information offered by energy analyses program. Otherwise, the studies considered the complete forecasted information about the quantity of solar irradiance in solar irradiance predictive method [
Paltridge et al. [
Lago et al. [
Benmouiza et al. [
The overall system architecture is shown in
The processes involved in overall system methodology are briefed herewith.
Initially, weather-related data is fed as input to BDAAI-SIP model and it is analyzed in big data analytics environment. Then, preprocessing is performed through three different stages such as data conversion, missing value replacement, and data normalization. Followed by, ENN-based predictive model is applied for prediction. This model makes use of a load bearing layer that transmits the state information and memory. Next, the parameter optimization of ENN model takes place using MFO algorithm to optimally determine the values of weights and hidden layer neuron count. Lastly, the performance of the BDAAI-SIP model is validated on benchmark dataset and the results are investigated in terms of different aspects.
In order to manage big data, Hadoop ecosystem and its components are widely applied. In a distributed atmosphere, Hadoop is a type of open-source design that allows a stakeholder to process big data on computer clusters with the help of simple programming systems. Since a single server has thousands of nodes, it can be simulated to involve improved scalability as well as fault tolerance. The three major components of Hadoop are MapReduce, Hadoop Distributed File System (HDFS), and Hadoop YARN.
Google File System (GFS) demonstrates HDFS as a structure of variety with master/slave, where master has more than one data node and is named after actual data whereas different name nodes are known to be metadata.
In order to offer massive scalability on thousand Hadoop clusters, Hadoop Map Reduce is utilized in the name of Apache Hadoop heart, a programming structure. To process huge information on massive clusters, MapReduce is utilized. Two essential stages are involved in MapReduce job modeling namely, Reduce and Map stage. All the stages contain key value pairs from input as well as output i.e., from the file system, combined output as well as input of the job are stored. The framework handles different tasks such as task scheduling, re-execution of the failed tasks and controlling the tasks. MapReduce framework contains one slave node control and a single master resource manager in every cluster node.
Hadoop YARN is a method utilized to manage the clusters. Based on the experience obtained from initial Hadoop generation, the second Hadoop generation is processed as an essential feature. YARN functions as a central structure and resource manager over Hadoop clusters in order to deal security, reliable functions, and data governance tools. In big data management, another platform device and components are installed on Hadoop framework.
Data pre-processing is an important part of AI technique and can considerably enhance the efficiency of the model. During data preprocessing in BDAAI model, the data initially undergoes conversion process in which the categorical values are transformed into numerical values. Besides, missing values’ replacement occurs to replace the missing values with alternate ones. Finally, min-max based data normalization process is applied to adjust the dataset to a uniform scale. In this technique, maximal and minimal values from a set data are examined. Every other data is normalized to these values. The purpose of normalization is to make the minimum value to zero and maximum value to one so that every other data is distributed in the range of 0 to 1.
ENN was presented by J. L. Elman to solve speech signal process in 1990 [
ENN is shown in
Here,
The input of the hidden layer is comprised of two portions namely context and external inputs, so,
The aim of this network is to reduce the error:
To reduce
The choice of parameters in ENN model is a crucial element to attain an effective classification outcome. Most of the ML models include multiple parameters that need to be optimized. Since trial-and-error method is infeasible, metaheuristic optimization based-MFO algorithm is applied in the selection of parameters. In general, the predictive error function acts as the objective function of MFO algorithm [
Every male mayfly and female mayfly upgrades its location in
Male mayfly swarms are performed with exploration or exploitation process during iterations. The velocity gets upgraded based on its present fitness value,
Conversely, when
Female mayflies upgrade their velocities through various styles. Biologically speaking, female winged-mayflies live only for a time span of 1–7 days. Thus, the female mayflies rush to detect the male mayflies for mating and reproduction. So, the velocities of female mayflies are upgraded according to male mayflies since it is required for mating purpose. In this MO technique, top optimal female and male mayflies are defined as the initial mate, and the second optimal female, male mayflies are defined as second mates, etc. Therefore, the i-th female mayfly, when
Every top half female and male mayfly is mated and produce a pair of children. Its offspring are arbitrarily developed by their parents:
In order to assess the predictive performance of BDAAI-SIP model, a set of simulations was conducted using HI-SEAS Solar Irradiance Prediction dataset sourced from Kaggle repository [
Concurrently, the BDAAI- SIP approach obtained lesser RMSE values such as 93.78, 94.86, and 93.69 on the applied training, testing, and validation datasets respectively. At the same time, on the applied fold-5, the BDAAI-SIP model reached the least MSE values such as 8253.723, 8675.060, and 8738.510 on the applied training, testing, and validation datasets respectively. Simultaneously, the BDAAI-SIP method accomplished low RMSE values such as 90.85, 93.14, and 93.48 on the applied training, testing, and validation datasets correspondingly. In addition, on the applied fold-7, the BDAAI- SIP method yielded minimum MSE values such as 8326.563, 8535.912, and 8764.704 on the applied training, testing, and validation datasets correspondingly. Followed by, the BDAAI-SIP model obtained lesser RMSE values such as 91.25, 92.39, and 93.62 on the applied training, testing, and validation datasets respectively. Moreover, on the applied fold-10, the BDAAI-SIP technique obtained the least MSE values such as 8753.474, 8222.862, and 8796.564 on the applied training, testing, and validation datasets respectively. At last, the BDAAI-SIP approach attained lesser RMSE values such as 93.56, 90.68, and 93.79 on the applied training, testing, and validation datasets correspondingly.
No. of folds | Training dataset | Testing dataset | Validation dataset | |||
---|---|---|---|---|---|---|
MSE | RMSE | MSE | RMSE | MSE | RMSE | |
1 | 8596.998 | 92.72 | 8596.998 | 93.68 | 8951.052 | 94.61 |
2 | 8517.444 | 92.29 | 8630.410 | 92.90 | 8866.106 | 94.16 |
3 | 8794.688 | 93.78 | 8998.420 | 94.86 | 8777.816 | 93.69 |
4 | 7986.997 | 89.37 | 8852.928 | 94.09 | 8971.878 | 94.72 |
5 | 8253.723 | 90.85 | 8675.060 | 93.14 | 8738.510 | 93.48 |
6 | 8574.760 | 92.60 | 8943.485 | 94.57 | 8901.923 | 94.35 |
7 | 8326.563 | 91.25 | 8535.912 | 92.39 | 8764.704 | 93.62 |
8 | 8682.512 | 93.18 | 8731.034 | 93.44 | 9064.944 | 95.21 |
9 | 8602.563 | 92.75 | 8436.423 | 91.85 | 8800.316 | 93.81 |
10 | 8753.474 | 93.56 | 8222.862 | 90.68 | 8796.564 | 93.79 |
Average | 8508.972 | 92.24 | 8680.248 | 93.16 | 8863.381 | 94.14 |
Irradiance (W/m2) | ||||||
---|---|---|---|---|---|---|
Time (h) | True values | LSTM | BPNN | Persistence | LR | BDAAI-SIP |
8 | 023.380 | 058.640 | 148.145 | 080.338 | 112.886 | 042.380 |
9 | 066.777 | 215.952 | 281.047 | 183.405 | 221.377 | 154.777 |
10 | 183.405 | 397.675 | 400.387 | 492.605 | 335.293 | 253.405 |
11 | 408.524 | 536.001 | 508.879 | 782.819 | 365.128 | 423.524 |
12 | 530.577 | 530.577 | 449.208 | 571.261 | 177.980 | 528.489 |
13 | 427.510 | 592.959 | 622.794 | 652.630 | 356.991 | 474.510 |
14 | 256.637 | 479.044 | 454.633 | 809.942 | 080.338 | 331.637 |
15 | 603.808 | 511.591 | 454.633 | 793.668 | 508.879 | 540.808 |
16 | 446.496 | 427.510 | 356.991 | 668.903 | 408.524 | 465.496 |
17 | 397.675 | 324.444 | 321.731 | 438.359 | 441.072 | 414.675 |
18 | 186.117 | 169.844 | 221.377 | 126.447 | 300.033 | 196.117 |
Besides, for a time period of 10 h, with its true value being 183.405 W/m2, the BDAAI-SIP model predicted the irradiance to be 253.405. While other methods such as LSTM, BPNN, Persistence, and LR approaches showcased inferior results with predictive irradiance values being 397.675, 400.387, 492.605, and 335.293 W/m2 respectively. Eventually, for a time period of 12 h, with a true value of 530.577 W/m2, the BDAAI-SIP model predicted the irradiance to be 528.489, whereas other methods such as LSTM, BPNN, Persistence, and LR approaches produced inferior outcomes with predictive irradiance values being 530.577, 449.208, 571.261, and 177.980 W/m2 respectively. Meanwhile, for a time period of 14 h, with a true value of 256.637 W/m2, the BDAAI-SIP model predicted the irradiance to be 331.637, whereas other methods such as LSTM, BPNN, Persistence, and LR techniques portrayed inferior results with the predictive irradiance of 479.044, 454.633, 809.942, and 080.338 W/m2 correspondingly.
Likewise, for a time period of 16 h, with 446.496 W/m2 true value, the BDAAI- SIP technique predicted the irradiance to be 465.496. On the other hand, other methods such as LSTM, BPNN, Persistence, and LR models exhibited inferior results with predictive irradiance values being 427.510, 356.991, 668.903, and 408.524 W/m2 respectively. At last, for a time period of 18 h, with a true value of 186.117 W/m2, the BDAAI- SIP model predicted the irradiance to be 196.117, whereas other models namely, LSTM, BPNN, Persistence, and LR models yielded inferior outcomes with predictive irradiance values being 169.884, 221.337, 126.447, and 300.033 W/m2 correspondingly.
Methods | Training dataset | Testing dataset | ||
---|---|---|---|---|
MSE | RMSE (W/m2) | MSE | RMSE (W/m2) | |
Persistence | - | - | 31339.97 | 177.031 |
LR | 40397.38 | 200.991 | 38367.02 | 195.875 |
BPNN | 17726.47 | 133.140 | 22585.43 | 150.284 |
LSTM | 10075.02 | 100.374 | 15059.56 | 122.717 |
GRU | 10893.10 | 104.370 | 14994.00 | 122.450 |
RNN | 15055.29 | 122.700 | 19329.34 | 139.030 |
CSpers | - | - | 29223.90 | 170.950 |
BDAAI-SIP | 8508.972 | 092.240 | 8680.248 | 093.160 |
The current research article designed a novel BDAA-SIP model to predict the solar irradiance using weather forecast data. Initially, weather related data are fed as input to BDAAI-SIP model which undergoes analysis on big data environment. Then, preprocessing is conducted through three different stages such as data conversion, missing value replacement, and data normalization. Followed by, ENN-based predictive model is applied in the prediction. This model makes use of a load bearing layer that transmits state information and memory. Next, the parameter optimization of ENN model takes place through MFO algorithm in order to optimally determine the values of weights and count of hidden layer neurons. Lastly, the performance of the proposed BDAAI-SIP model was validated on benchmark datasets and the results were investigated under different aspects. To examine the efficacy of BDAAI- SIP model, a set of simulations was conducted. The experimental values highlight that the proposed method yielded better performance than the compared methods. As a part of future scope, the presented model can be extended to design the next day SIP model using weather forecast data.