Abstract:
Zambia has faced persistent energy shortages over the past decade due to heavy
reliance on hydropower. Recent droughts, exacerbated by climate variability, have highlighted
the vulnerability of this dependence and accelerated the country’s diversification
toward solar energy. Effective integration of solar generation into the national grid
requires accurate forecasting of global horizontal irradiance (Global Horizontal Irradiance
(GHI)) to mitigate intermittency and support efficient energy planning. Despite
increasing investment in solar power, limited research exists on forecasting models tailored
to Zambia’s climatic conditions.
This study develops a data-driven GHI forecasting framework using ten years of local
meteorological data, including GHI, temperature, humidity, precipitation, and wind
speed, sourced from the Zambia Meteorological Department (ZMD) and Joint Research
Center Photovoltaic Geographical Information System (JRC PVGIS). A systematic literature
review guided by the Preferred Reporting Items for Systematic Reviews and Meta-
Analyses (PRISMA) framework and bibliometric analysis examined global, regional, and
local trends in solar irradiance forecasting. Based on the review, three widely applied
machine learning models—Long Short-Term Memory (LSTM), Random Forest (RF),
and Artificial Neural Networks (ANN)—were selected for evaluation. Feature selection
employed Variance Inflation Factor (VIF) analysis and Least Absolute Shrinkage and Selection
Operator (LASSO) regression, identifying temperature and humidity as the most
relevant predictors.
The models were trained and tested using consistent data splits and evaluated using
Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R². Results indicate that the ANN model achieved the highest predictive accuracy (MAE = 7.378
W/m², RMSE = 9.584 W/m², R² = 0.845), followed by RF (MAE = 9.845 W/m², RMSE
= 12.374 W/m², R² = 0.715) and LSTM (MAE = 0.589 W/m², RMSE = 0.739 W/m²,
R² = 0.392). These findings demonstrate that reliable short-term GHI forecasting can
be achieved using locally relevant predictors and accessible data.
The study provides practical value for energy planners, utilities, and policymakers
seeking to enhance grid stability, optimize solar dispatch, and improve renewable energy
integration in Zambia. It recommends adopting ANN-based forecasting tools and further
exploring hybrid and region-specific approaches supported by enhanced local climate data
collection.
