Photovoltaic Energy Production Forecasting through Machine Learning Methods: A Scottish Solar Farm Case Study

Abstract

Photovoltaic solar energy is booming due to the continuous improvement in photovoltaic panel efficiency along with a downward trend in production costs. In addition, the European Union is committed to easing the implementation of renewable energy in many companies in order to obtain funding to install their own panels. Nonetheless, the nature of solar energy is intermittent and uncontrollable. This leads us to an uncertain scenario which may cause instability in photovoltaic systems. This research addresses this problem by implementing intelligent models to predict the production of solar energy. Real data from a solar farm in Scotland was utilized in this study. Finally, the models were able to accurately predict the energy to be produced in the next hour using historical information as predictor variables.

Current Challenges in Operation, Performance, and Maintenance of Photovoltaic Panels

“…Machine Learning Tool Note [142] eXtreme Gradient Boosting, Light Gradient Boosting, MultiLayer Perceptron, Elman Neural Network, Long Short-Term Memory comparative study [143] MultiLayer As Table 2 illustrates, the forecast of PV generation is a well-known challenge with many proposed answers. Nonetheless, the accuracy of PV forecasts is still limited because the architecture of forecasting algorithms (including those applying AI) is built on inherently inaccurate weather forecasts.…”

Advances in Energy Efficiency through Neural-Network-Based Models

“…The authors validated their models with actual energy data that were experimentally measured. In [2], the authors applied some of the most well-known machine learning methods to a solar farm in Scotland. Furthermore, energy consumption modelling was performed in order to optimise energy use and predict scenarios such as electricity expenditure in the industrial sector by combining neural-based models with statistical methods [3] or fuel consumption for vehicles by making use of technical parameters and artificial neural networks [4].…”

Time Series Analysis and Forecasting of Solar Generation in Spain Using eXtreme Gradient Boosting: A Machine Learning Approach

“…This section delves into the most recent research literature within the area of study, highlighting the extensive range of solar power models and techniques that have been proposed. These models encompass various mathematical functions, both linear and nonlinear, applied across diverse contexts, including projects in Saudi Arabia [9], Malaysia [10], Brazil [11], Israel [12], Australia [13,14], Turkey [15], India [16], the United States [17], Scotland [18], South Korea [19], Nigeria [20], Italy [21], and Algeria [22]. Moreover, non-linear functions have been employed for daily diffuse solar energy radiation calculations [23], irradiation simulations [24], and unrestricted methods [25].…”