Solar　radiation　has　important　impacts　on　buildings　such　as　for　cooling/heating　load　forecasting,　energy　consumption　forecasting,　and　multi-energy　complementary　optimization.　Two　types　of　solar　radiation　data　are　commonly　used　in　buildings:　radiation　data　in　typical　meteorological　years　and　measured　radiation　data　from　meteorological　stations,　both　of　which　are　types　of　historical　data.　However,　it　is　difficult　to　predict　the　hourly　global　solar　radiation,　which　affects　the　application　of　relevant　prediction　models　in　practical　engineering.　Most　existing　methods　for　predicting　hourly　global　solar　radiation　have　issues　such　as　difficulty　in　obtaining　input　parameters　or　complex　data　processing,　which　limits　their　practical　engineering　applications.　This　study　proposed　a　simplified　method　to　accurately　predict　the　hourly　horizontal　solar　radiation　using　extraterrestrial　solar　radiation,　weather　types,　cloud　cover,　air　temperature,　relative　humidity,　and　time　as　the　input　parameters.　The　back-propagation　network,　support　vector　machine,　and　light　gradient　boosting　machine　(LightGBM)　models　were　used　to　establish　the　prediction　model,　and　Shapley　additive　explanations　were　used　to　analyze　the　relationship　between　the　input　variables　and　the　prediction　results　to　simplify　the　structure　of　the　prediction　model.　Taking　Lanzhou　New　District　in　Gansu　Province　as　an　example,　the　results　showed　that　the　LightGBM　model　performed　the　best,　with　the　root　mean　square　error　of　126.1　W/m2.　Shapley　additive　explanations　analysis　showed　that　weather　type　was　not　a　significant　factor　in　the　LightGBM　model.　Therefore,　the　weather　type　was　removed　from　the　LightGBM　model　and　the　root　mean　square　error　was　135.2　W/m2.　The　results　showed　that　extra-terrestrial　radiation　and　limited　weather　forecast　parameters　can　be　used　to　predict　hourly　global　solar　radiation　with　satisfactory　prediction　results.　©　2023　by　the　authors.