In general, sixty percent of a building's energy is consumed for ventilation, air-conditioning, and heating functions [1,2]. This energy could be saved through optimal control of heating and air-conditioning operations of the building. One of the major problems faced in terms of future global energy source is the combination of renewable energy source (mainly non-predictable ones such as solar and wind) to produce energy from current or upcoming energy sources. It is a must for an electrical operator to guarantee a proper balance between electricity production and consumption at any time. However, the operator faces several challenges during most of the times to preserve this balance with controllable and conventional energy production methods, mostly in small or not interrelated (i.e., isolated) electricity networks (that originate in an island, for example). The consistency of electric grid is decided based on the capacity of network to meet the unexpected and expected variations (i.e., in terms of consumption and production) and conflicts, while at the same time, preserving continuity and quality of facility to the consumers in a seamless manner. Afterwards, the energy provider should be able to handle the network with several time-based horizons [3].

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 [4]. Solar energy production is a non-controllable and intermittent energy source due to which various challenges are faced like local power quality, stability issues, and voltage fluctuations. Therefore, predicting the output power of solar PV system is essential for efficient functioning of electrical network or optimum management of energy flows that occur in solar PV system [5]. It is also required in electric network scheduling, resource estimation, optimum management of storage with stochastic production, congestion management, cost reduction in the production of electricity, and finally trade the energy generated in electricity market. It has become highly significant to predict the production of energy from solar PV since there is a significant rise in solar power production in the recent years. To prevent huge differences in renewable electricity generation, it is essential to involve the whole predictive system operation with storage results.

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 [9]. In general, solar irradiance predictive technique is either based on data or physics [10]. Physical method is commonly established depending upon solar geometry to create an experimental connection between solar irradiance data and meteorological variables measured in previous monitoring areas [11]. Black [12] established a method to predict solar irradiance by examining the connection between sky cover and solar irradiance data collected for a period of 3 years in an area. Likewise, Samimi [13] established a solar irradiance method with high accuracy in which the researcher utilized climate data of Iran collected over 17 years.

Paltridge et al. [14] proposed a physics-based solar irradiance prediction method utilizing several climate variables like long-term accumulated data, humidity, wind, and precipitation. However, according to Premalatha et al. [15], to define solar irradiance coefficient in an area under study, physics-based weather prediction method needs long-term measured data or information that is complex for protecting, in typical weather predicting data. Thus, this method could not be employed in the prediction of upcoming day solar irradiance. Solar irradiance method that depends upon physical method, is established to calculate annual/monthly overall solar irradiance, instead of real-world predictive methods like MPC application [16].

Lago et al. [17] stated that the NN framework is beneficial in prediction and evaluation of the time sequential information with high arbitrariness. Jiang [18] stated that ANN predictive method demonstrates high accuracy than the experimental physical solar irradiance predictive method. Solar irradiance predictive method was introduced earlier based on learning data and it integrates several learning techniques based on the objective. Sharma et al. [19] proposed a method that grasps the scenario in 15 min period whereas, Kemoku et al. [20] conducted an investigation using FFNN to predict upcoming day solar irradiance by learning solar irradiance information for previous 6 years in Japan. Ahmad et al. [21] performed an investigation to identify the optimal integration of input variables to predict the solar irradiance including climate integration of 12 conditions of solar irradiance predictive in New Zealand.

Benmouiza et al. [22] presented a learning technique different from existing ANN models to predict solar irradiance. The investigations on ANN-based solar irradiance predictive method are widely performed and in recent times, the predictive method does not utilize the information attained from developed land. In order to predict the local solar irradiance, Rodríguez et al. [23] proposed an ANN method that grasps information attained from a satellite. This study considered six years data collected from various places. Srivastava et al. [24] introduced a solar irradiance predictive method which analyzes a major quantity of satellite data collected from different European countries. This method was utilized for studying nine years of climate conditions in 21 cities across Europe and US.

The overall system architecture is shown in Fig. 1. As shown in the figure, the proposed BDAAI-SIP model undergoes three major processes namely, data preprocessing, predictive analysis, and parameter optimization. Besides, the Hadoop MapReduce tool is also applied to handle the massive collection of weather forecast data.

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 [28]. The dataset holds weather-related details from HI-SEAS Habitat in Hawaii. Particularly, the dataset comprises of the following parameters namely, Solar Irradiance (W/m²), Temperature (°F), Barometric Pressure (Hg), Humidity (%), Wind Direction (°), Wind Speed (mph), and Sun Rise/Set Time. The results were validated under two measures such as Mean Square Error (MSE) and Root Mean Square Error (RMSE) methods.

Tab. 1 and Fig. 3 shows the results of the analysis achieved by BDAAI-SIP model in terms of MSE and RMSE on the applied dataset. From the table, it is evident that the BDAAI-SIP model attained minimal MSE and RMSE values for training, testing, and validation datasets. For instance, on the applied fold-1, the BDAAI-SIP model achieved the least MSE values such as 8596.998, 8596.998, and 8951.052 on the applied training, testing, and validation datasets respectively. Similarly, the BDAAI-SIP model obtained minimal RMSE values such as 92.72, 93.68, and 94.61 on the applied training, testing, and validation datasets respectively. Likewise, on the applied fold-3, the BDAAI-SIP technique accomplished minimum MSE values such as 8794.688, 8998.420, and 8777.816 on the applied training, testing, and validation datasets correspondingly.

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.

Fig. 4 shows the results of average RMSE analysis for the proposed BDAAI-SIP model on the applied dataset. From the figure, it is understood that the proposed BDAAI-SIP model has demonstrated better results since it achieved an effective performance through maximum average RMSE values such as 92.24, 93.16, and 94.14 on the applied training, testing, and validation datasets respectively.

Tab. 2 and Fig. 5 shows the results yielded by BDAAI-SIP model under varying times in terms of predicted irradiance. From the table, it can be understood that BDAAI-SIP model appropriately predicted the irradiance. In other terms, the difference from actual true value is considerably lower than the compared methods. For instance, for a time period of 8 h with true value being 23.380 W/m², the BDAAI-SIP model has predicted the irradiance to be 42.380, whereas other methods such as LSTM, BPNN, Persistence, and LR models predicted inferior results for predictive irradiance such as 058.640, 148.145, 080.338, and 112.886 W/m² respectively.

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.

Tab. 3 and Fig. 6 provides a brief comparison of MSE and RMSE values obtained by the proposed BDAAI-SIP model and existing methods on the applied training and testing datasets [29,30]. From the results, it is evident that the LR model showcased ineffective predictive results with its RMSE values being 200.991 and 195.875 W/m² on training and testing datasets respectively. At the same time, the Persistence model displayed a slightly higher outcome with its RMSE value being 177.031 W/m² on the applied testing dataset. Next to that, the BPNN model has yielded moderate RMSE values such as 17726.47 and 22585.43 W/m² on training and testing datasets respectively. The RNN model accomplished an improved outcome with its RMSE values being 15055.29 and 122.700 W/m² on training and testing datasets respectively. In line with this, both LSTM and GRU methods demonstrated a closer and reasonable outcome over other methods. But the presented model outperformed other models through better results i.e., least RMSE values such as 092.240 and 93.160 W/m² upon training and testing datasets respectively. From the above discussed results of the analysis, it is evident that the presented BDAAI-SIP model is an effective predictive tool for solar irradiation.

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.