Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar

Abstract

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.

Autonomous Installations for Monitoring the “Protector Prosperina“ Forest

“…In addition, for the lighting of a 10 W lamp, which must be lit for 2 h during the night (18:00-20:00 pm), to benefit hikers or visitors. Finally, the installation of an electric current meter allows us to keep track of the amount of energy that is consumed by visitors to the forest [16].…”

“…The trail "El Mirador" was evaluated throughout the year 2018, and the fauna was documented along the trail using field census techniques for both invertebrates and vertebrates [17] so several censuses were conducted where animals were recorded to each side of the trail. Moreover, the plant cover was evaluated by quadrants of 5 × 5 m placed 5 m off at one side of the path, at a relative distance of 750 m from the beginning of the trail, located 120 m above sea level to highest point at 210 m above sea level [16].…”

A Machine Learning Approach to Low-Cost Photovoltaic Power Prediction Based on Publicly Available Weather Reports

“…Rosato et al [14] proposed three predictive approaches based on ANN, which were used to predict power output for a large-scale PV plant in Italy. An ANN model was also used by Khandakar et al [15] to predict the PV power output in Qatar, but the authors of this study additionally investigated two different feature selection techniques. In References [16][17][18], it was discussed that the application of long short-term memory (LSTM) neural networks provides particularly good results in PV power forecasting.…”

Genetic Algorithm Approach to Design of Multi-Layer Perceptron for Combined Cycle Power Plant Electrical Power Output Estimation

“…ANN for air-temperature predictions in smart buildings was developed in [31] in order to obtain better energy control. Short term forecasting prediction of the photovoltaic plant power output by using ANNs can be found in [32,33]. Analysis of heating expenses in a large social housing stock using artificial neural networks is presented in [34].…”