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Soiling significantly impacts PV systems’ performance, but this can be mitigated through optimized frequency and timing of cleaning. This experimental study focused on the conditions leading to soiling. It utilized a novel method to evaluate the effectiveness of different cleaning frequencies. The transmittance of horizontally mounted glass coupons exposed outdoors in a warm and humid location was measured weekly and these measurements were used (i) to evaluate the variability of soiling and its seasonal correlations with environmental factors using linear regression models and (ii) to assess the effectiveness of the different cleaning cycles using statistical (F- and t-test) analysis. The minimum transmittance loss occurred during the season with the most frequent rainfall, which acted as the dominant natural cleaning agent. The experimental campaign showed that rainfalls do not completely clean soiling; a minimum intensity threshold has to be achieved to have a cleaning effect. The threshold rainfall was the highest for the weekly cleaned glass coupon and lowest for a coupon that was never cleaned. Based on the statistical analysis, it is suggested that weekly cleanings during winter and post-monsoon seasons and monthly cleanings during pre-monsoon and southwest monsoon seasons are optimal for areas in the Köppen–Geiger Cwa climate classification category. The correlation between soiling and environmental parameters was found to be highly dependent on the season. It may therefore not be possible to develop a simple, universal predictive relationship for soiling losses. The presented methodology is applicable to additional locations, even outside of the study area of India, to contribute to the understanding and mitigation of soiling.

This paper calculates and analyzes the collision and freezing characteristics of water droplets on the cylindrical conductors with different diameters and deduces the variation law of icing weight change rate on different diameters rotating cylindrical conductors with wind speed, ambient temperature, liquid water content, and median volume diameter. Then, a prediction and value-seeking method of icing environment parameters based on rotating circular conductors is determined. The laboratory test results show that the measured values of ambient temperature T, wind speed U, liquid water content w and median volume diameter MVD are in good conformity with the reference values measured by other instruments, and the average errors of each parameter are 3.41%, 5.44%, 6.08% and 7.20%, respectively. The field natural experiment was carried out at Hunan Xuefengshan Energy Equipment Safety National Observation and Research Station. The obtained icing environmental parameters were substituted into the icing simulation calculation model of the LDQ-100 glass insulator. Compared with the field experiment results, the simulated ice shape and the field ice shape were in good agreement. The error between the numerical calculation results of the icing weight and the field icing weight of the experimental insulator was less than 6%.

Photovoltaic (PV) systems have become one of the most promising alternative energy sources, as they transform the sun’s energy into electricity. This can frequently be achieved without causing any potential harm to the environment. Although their usage in residential places and building sectors has notably increased, PV systems are regarded as unpredictable, changeable, and irregular power sources. This is because, in line with the system’s geographic region, the power output depends to a certain extent on the atmospheric environment, which can vary drastically. Therefore, artificial intelligence (AI)-based approaches are extensively employed to examine the effects of climate change on solar power. Then, the most optimal AI algorithm is used to predict the generated power. In this study, we used machine learning (ML)-based algorithms to predict the generated power of a PV system for residential buildings. Using a PV system, Pyranometers, and weather station data amassed from a station at King Khalid University, Abha (Saudi Arabia) with a residential setting, we conducted several experiments to evaluate the predictability of various well-known ML algorithms from the generated power. A backward feature-elimination technique was applied to find the most relevant set of features. Among all the ML prediction models used in the work, the deep-learning-based model provided the minimum errors with the minimum set of features (approximately seven features). When the feature set is greater than ten features, the polynomial regression model shows the best prediction, with minimal errors. Comparing all the prediction models, the highest errors were associated with the linear regression model. In general, it was observed that with a small number of features, the prediction models could minimize the generated power prediction’s mean squared error value to approximately 0.15.

Smart buildings use Internet of Things (IoT) sensors for monitoring indoor environmental parameters, such as temperature, humidity, luminosity, and air quality. Due to the huge amount of data generated by these sensors, data analytics and machine learning techniques are needed to extract useful and interesting insights, which provide the input for the building optimization in terms of energy-saving, occupants’ health and comfort. In this paper, we propose an IoT-based smart building (SB) solution for indoor environment management, which aims to provide the following main functionalities: monitoring of the room environmental parameters; detection of the number of occupants in the room; a cloud platform where virtual entities collect the data acquired by the sensors and virtual super entities perform data analysis tasks using machine learning algorithms; a control dashboard for the management and control of the building. With our prototype, we collected data for 10 days, and we built two prediction models: a classification model that predicts the number of occupants based on the monitored environmental parameters (average accuracy of 99.5%), and a regression model that predicts the total volatile organic compound (TVOC) values based on the environmental parameters and the number of occupants (Pearson correlation coefficient of 0.939).

Photovoltaics (PV) output power is highly sensitive to many environmental parameters and the power produced by the PV systems is significantly affected by the harsh environments. The annual PV power density of around 2000 kWh/m2 in the Arabian Peninsula is an exploitable wealth of energy source. These countries plan to increase the contribution of power from renewable energy (RE) over the years. Due to its abundance, the focus of RE is on solar energy. Evaluation and analysis of PV performance in terms of predicting the output PV power with less error demands investigation of the effects of relevant environmental parameters on its performance. In this paper, the authors have studied the effects of the relevant environmental parameters, such as irradiance, relative humidity, ambient temperature, wind speed, PV surface temperature and accumulated dust on the output power of the PV panel. Calibration of several sensors for an in-house built PV system was described. Several multiple regression models and artificial neural network (ANN)-based prediction models were trained and tested to forecast the hourly power output of the PV system. The ANN models with all the features and features selected using correlation feature selection (CFS) and relief feature selection (ReliefF) techniques were found to successfully predict PV output power with Root Mean Square Error (RMSE) of 2.1436, 6.1555, and 5.5351, respectively. Two different bias calculation techniques were used to evaluate the instances of biased prediction, which can be utilized to reduce bias to improve accuracy. The ANN model outperforms other regression models, such as a linear regression model, M5P decision tree and gaussian process regression (GPR) model. This will have a noteworthy contribution in scaling the PV deployment in countries like Qatar and increase the share of PV power in the national power production.

The production of energy at the global level is conditioned by the use of fossil fuels that have a great environmental impact. In the last decades, renewable energy production systems have been implemented, and networks of nearly zero-energy buildings have been created, with a consequent complexity in the design phase in order to optimize the results. In this way, electronic prototype development methods like the one that is proposed in this paper improve the tasks of design and modelling. Thus, a new weather station based on an Arduino platform has been developed to collect and store ambient temperature, relative humidity, barometric pressure, wind speed and air quality data, comparing the obtained data to those obtained using a validation station containing commercial sensors. The results show how the use of low cost Arduino sensors allow one to obtain similar values to those collected by more professional meteorological stations with insignificant scatter between both technologies.