The　load　forecast　of　power　system　is　one　of　the　important　tasks　of　power　dispatch　and　service　department,　whose　accuracy　has　a　close　relation　with　dispatch　operation,　production　plan　and　quality　of　power　supply.　So　going　further　to　the　research　of　load　forecasting　method　and　model　has　important　realistic　meanings.　The　hybrid　algorithm　which　combines　the　particle　swarm　optimization　with　BP　neural　network　is　applied　in　the　short-term　daily　load　forecasting　of　power　system,　and　it　has　a　good　accuracy.　The　basic　principle　of　particle　swarm　optimization　algorithm　is　discussed,　and　the　operational　method　and　procedure　of　the　particle　swarm-neural　network　hybrid　algorithm　are　given.　Then　by　the　XOR　problem,　the　PSO-BP　hybrid　algorithm　is　proved　to　have　a　quicker　convergence　and　a　higher　computational　precision.　Then,　the　daily　load　forecasting　model　using　the　PSO-BP　hybrid　algorithm　is　established,　which　has　been　applied　in　the　daily　load　forecasting　of　a　city.　Compared　the　predicted　results　with　other　used　methods,　the　PSO-BP　hybrid　algorithm　is　proved　to　have　a　higher　prediction　accuracy:　the　average　relative　error　is　not　more　than　1.48%　and　the　maximum　relative　error　is　not　more　than　4.10%.　From　the　comparison　we　could　find　that　while　the　PSO-BP　hybrid　algorithm　is　applied　in　the　short-term　load　forecasting,　it　has　a　quick　convergence,　a　high　computational　precision　and　a　low　error,　the　predicted　results　could　meet　the　basic　requirements　of　error　in　short-term　load　forecasting.