Enhancing Solar Power Forecasting Accuracy Using HMPCS and Machine Learning Techniques: An Applied Study
Asmaa Abdul-Hussein Aziz, Iraq T. Abbas, Ashif Mohammad, Bin Du, Bin Du, iraq abbas, Ashif Mohammad, iraq abbas · 2026
Read Enhancing Solar Power Forecasting Accuracy Using HMPCS and Machine Learning Techniques: An Applied Study by Asmaa Abdul-Hussein Aziz, Iraq T. Abbas, Ashif Mohammad, Bin Du, Bin Du, iraq abbas, Ashif Mohammad, iraq abbas (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13589176.txt.
Description
Solar power forecasts are sharpened by Asmaa Abdul-Hussein Aziz and Iraq T. Abbas in applied work dated 2026. Sunlight swings by hour and weather, which makes power output hard to call. Their tool joins a cuckoo search variant with machine learning models. The search tunes model settings by flock-like spread and random long jumps, across more than one group at once.
Fake weather and sunlight data fed the tests, cleaned and scaled first. A tuned memory-based model gave the lowest error and the best fit of all pairs tried. A tuned tree model also beat its plain form by a wide edge. The joined frame beat two standard tuning ways.
Catalog record
| CREATOR | Asmaa Abdul-Hussein Aziz, Iraq T. Abbas, Ashif Mohammad, Bin Du, Bin Du, iraq abbas, Ashif Mohammad, iraq abbas |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 77 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |