What question did this study set out to answer?

The aim is to analyze electricity demand and improve load forecasting for rural mini grids in Uganda.

June 20, 2026Tanzania Journal of ScienceOpen Access

Load profile analysis and forecasting for rural mini grids in Uganda

View Full Paper

Ask AI

Bookmark

The aim is to analyze electricity demand and improve load forecasting for rural mini grids in Uganda.
Examined diurnal load profiles and seasonal variability using 2022 operational data from Buzaami and Ssenyondo mini grids.
Compared forecasting models: Long Short-Term Memory (LSTM) networks, fuzzy logic models, and a hybrid LSTM–fuzzy framework.
Evaluated forecasting accuracy using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE).
The hybrid LSTM–fuzzy model achieved lower MAPE and RMSE compared to standalone models, indicating better forecasting performance.
Robustness and interpretability of the hybrid model surpassed those of the pure LSTM approach.
Enhanced forecasting supports optimized dispatch and reduces reliance on backup generation during peak demand periods.

Discussion

No takes yet. Share an insight, caveat, or question.

Implication

Randomized trial evaluates forecasting models to improve reliability and reduce costs in rural mini grids, indicating significant operational benefits.

Cite This Study

Mutesi et al. (2026) studied this question.

synapsesocial.com/papers/6a36309edb0793dc1a537b43 https://doi.org/10.65085/2507-7961.2251

View Full Paper

Ask AI

Bookmark

Implication

Randomized trial evaluates forecasting models to improve reliability and reduce costs in rural mini grids, indicating significant operational benefits.

Load profile analysis and forecasting for rural mini grids in Uganda

Key Points

Discussion

Implication

Cite This Study

Load profile analysis and forecasting for rural mini grids in Uganda

Key Points

Discussion

Implication

Cite This Study

Also Consider

Also Consider