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Case Studies | Renewable and Sustainable Energy | South Africa | Volume 9 Issue 9, September 2020
Forecasting Photovoltaic Electricity Generation at a Private Dwelling in Tshwane, South Africa: A Case Study
M. A. Smit | D. M. Schoeman | F. C. Rust
Abstract: Green House Gas (GHG) emission and its detrimental effects are by now a well-known phenomenon. Many countries are focussing on reducing their carbon footprint as a result. This has become evident in the move towards alternative, green energy sources such as wind, biogas and solar energy applications. In this paper, the forecasting of solar power generated through a solar installation at a private house in Tshwane, South Africa is discussed. The system comprised 12 photovoltaic (PV) panels, an inverter, two lithium batteries and one solar thermal geyser. Weather data from a nearby weather station was used to develop a model for the prediction of solar energy generated. The inverter provides a web-based interface where output and usage of the electricity is recorded. This data was used of over a one-year period and analysed, to develop a PV electricity forecasting model. The variables included ambient temperature, cloud opacity, Global Horizontal Irradiance, Global Tilted Irradiation (Gti) - Fixed Tilt and Gti Sun Tracking. Both multiple linear regression and Long Short Term Memory (LSTM) models were used to assess their ability to forecast PV electricity generation. The potential PV electricity to be generated for the previous five years was then calculated using historical weather data. Based on the assumption that the next five years would have similar weather patterns, the PV electricity forecasted was calculated. The savings potential and break-even time for the installation were then calculated based on the forecasted PV electricity generation. The break-even periods were calculated for a 5 %, 10 % and 15 % electricity increase per year and ranged from 8 years to 12, 3 years. The additional benefits of the systems and possible areas for improvement are also discussed.
Keywords: PV power, solar power generation prediction model, domestic PV, off-grid domestic power
Edition: Volume 9 Issue 9, September 2020,
Pages: 1014 - 1027