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The advancement of Photovoltaic technology has undergone rapid acceleration in recent years. Nonetheless, the most significant drawback of Photovoltaic is its intermittence, making it an obvious source of power fluctuation. This study proposes a novel scheme for real‐time or intraday PV power forecasting by adopting two predictive models, namely, White‐box and Combination. The White‐box model is implemented employing mathematical calculations and statistics called Exceedance Probability. Meanwhile, the Combination model is an aggregation of several predictive models' outputs including the White‐box model and benchmark ones by dynamically adjusting the weight coefficient of each model based on their forecasting accuracy. The experimental results, which are verified on two PV systems corresponding to two case studies located at Vietnam and Australia, indicate that the two proposed models outperform other referenced models as nMAPE$\mathrm{nMAPE}$ improves approximately 40% and 38% in terms of the first and second case study, respectively. In particular, the White‐box model shows superiority by updating the forecast every 10 min, which can adapt to the fluctuation of weather conditions whereas the Combination one yields acceptable precision, indicating its flexible application. [ABSTRACT FROM AUTHOR]

Recently, the increasing penetration of renewable energy resources in power system, especially photovoltaic (PV) systems, has caused severe technical issues due to its randomness and dependency on primary sources. Therefore, precise output power forecast is vital for both system operators and PV system owners to improve grid stability and generation quality, respectively. This article proposes a novel hybrid deep learning model named EDSACL comprising four components, namely, convolutional neural network (CNN), long short‐term memory (LSTM), self‐attention mechanism (SAm), and residual learning (RL) strategy for short‐term PV maximum power prediction with forecast horizon of 1, 2, and 4 h. This proposed model initially extracts spatial‐temporal features of input data based on CNN and LSTM before using SAm to achieve hidden features of LSTM layers. RL strategy is employed to maintain the information flow entering the network. Besides, interval prediction is also implemented based on a combination of the EDSACL model and quantile regression with different probabilities. The forecast accuracy of the proposed model is validated based on two real‐world data sets. The predicted results show the superiority of the proposed model over numerous predictive models; in particular, mean absolute percentage error values of the proposed hybrid model are under 6% in all case studies. [ABSTRACT FROM AUTHOR]

Smart buildings that utilize innovative technologies such as artificial intelligence (AI), the internet of things (IoT), and cloud computing to improve comfort and reduce energy waste are gaining popularity. Smart buildings comprise a range of sensors to measure real-time indoor environment variables essential for the heating, ventilation, and air conditioning (HVAC) system control strategies. For accuracy and smooth operation, current HVAC system control strategies require multiple sensors to capture the indoor environment variables. However, using too many sensors creates an extensive network that is costly and complex to maintain. Our proposed research solves the mentioned problem by implementing a machine-learning algorithm to estimate unmeasured variables utilizing a limited number of sensors. Using a six-month data set collected from a three-story smart building in Japan, several extreme gradient boosting (XGBoost) models were designed and trained to estimate unmeasured room temperature, relative humidity, and CO2 concentrations. Our models accurately estimated temperature, humidity, and CO2 concentration under various case studies with an average root mean squared error (RMSE) of 0.3 degrees, 2.6%, and 26.25 ppm, respectively. Obtained results show an accurate estimation of indoor environment measurements that is applicable for optimal HVAC system control in smart buildings with a reduced number of required sensors. [ABSTRACT FROM AUTHOR]

The dramatic penetration of sustainable energy systems, including photovoltaic (PV) systems, has profoundly affected the stability and the reliability of the power system. Hence, determining the generation characteristics of PV systems has been becoming a crucial task in planning and dispatching the power system. However, since the generation of PV systems is profoundly impacted by the weather conditions, especially the irradiance and the temperature, producing a PV model, which performs well at various conditions, has been a challenging task for years. In this paper, we present a model to determine the PV generation characteristics by obtaining the current‐voltage (I–V) curves of the single‐diode model. First, the three key points, including the open‐circuit point, the short‐circuit point, and the maximum power point, are estimated from the historical data based on an improved regression model. After that, a new algorithm is proposed, which ensures the model always converges at any temperature and irradiance conditions. The proposed model is validated by comparing with recently published models and the measured data from the National Renewable Energy Laboratory, USA. Compared to previous numerical models, the proposed model is capable of determining the generation characteristics of PV systems at anomaly conditions (anomalously low irradiance and anomalously high irradiance) with high accuracy and fast speed. The validated results demonstrate the accuracy and the reliability of the proposed model in determining the generation characteristics of PV modules at real‐time conditions. [ABSTRACT FROM AUTHOR]