Application　of　cloud　computing　technologies　in　power　system　has　made　a　great　contribution　to　the　establishment　of　smart　grid.　Among　applications　of　smart　grid,　electrical　load　prediction　plays　an　important　role　in　efficient　use　of　power　resource.　However,　the　exponential　growth　of　data　has　posed　a　great　challenge　to　the　existing　algorithms.　In　this　paper,　we　firstly　propose　a　novel　parallel　hybrid　algorithm,　combining　the　Improved　Particle　Swarm　Optimization　(PSO)　with　ELM,　named　PIPSO-ELM.　Here　a　modified　particle　swarm　optimization　is　presented　to　find　the　optimal　number　of　hidden　neurons　as　well　as　the　corresponding　input　weights　and　hidden　biases.　Furthermore,　in　the　iterative　search　process　of　PSO,　an　update　strategy　employs　the　mutation　operator　of　evolutionary　algorithms　is　introduced　for　further　improving　the　global　search　capability　and　convergence　speed　of　PSO.　After　that,　to　handle　the　large-scale　dataset　efficiently,　the　parallel　implementation　of　PIPSO-ELM　is　achieved　using　Spark.　Finally,　extensive　experiments　on　real-life　electrical　load　data　and　comprehensive　evaluation　are　conducted　to　verify　the　performance　of　PIPSO-ELM　in　electrical　load　prediction.　Extensive　experimental　results　demonstrate　that　PIPSO-ELM　outperforms　the　compared　algorithms　in　terms　of　stability,　efficiency　and　scalability　simultaneously.　©　2017　IEEE.