The　fluctuations　in　weather　conditions,　such　as　temperature　and　wind　speed,　can　impact　the　process　of　solar　and　wind　power　generation,　thereby　influencing　electricity　prices.　Real-time　price　settlement　requires　a　higher　resolution　than　daily　frequency　to　forecast　electricity　prices.　This　research　proposes　a　novel　mixed-frequency　model　to　address　the　issue　of　frequency　inconsistency　problem　between　electricity　prices　and　related　factors,　and　successfully　applies　it　to　Belgium　electricity　price　forecasting.　Firstly,　the　RU-MIDAS　method　is　used　to　analyze　the　dynamic　impact　of　weather　conditions　on　prices.　Then,　RU-MIDAS　is　combined　with　machine　learning　algorithms　to　predict　the　prices.　The　results　of　error　metrics　and　MCS　test　indicate　that　humidity　can　improve　the　prediction　accuracy　of　all　four　sequences.　The　prediction　ability　of　wind　gusts　is　comparable　to　that　of　the　highest　price;　the　former　helps　predict　the　last　two　subsequences,　while　the　latter　improves　the　prediction　accuracy　of　the　first　two　subsequences.　Temperature　can　only　help　predict　the　fourth　electricity　price　series,　and　the　forecasting　ability　of　weather　factors　is　consistent　with　the　order　of　feature　importance.