A　recent　surge　of　interest　in　building　energy　consumption　has　generated　a　tremendous　amount　of　energy　data,　which　boosts　the　data-driven　algorithms　for　broad　application　throughout　the　building　industry.　This　article　reviews　the　prevailing　data-driven　approaches　used　in　building　energy　analysis　under　different　archetypes　and　granularities,　including　those　methods　for　prediction　(artificial　neural　networks,　support　vector　machines,　statistical　regression,　decision　tree　and　genetic　algorithm)　and　those　methods　for　classification　(K-mean　clustering,　self-organizing　map　and　hierarchy　clustering).　The　review　results　demonstrate　that　the　data-driven　approaches　have　well　addressed　a　large　variety　of　building　energy　related　applications,　such　as　load　forecasting　and　prediction,　energy　pattern　profiling,　regional　energy-consumption　mapping,　benchmarking　for　building　stocks,　global　retrofit　strategies　and　guideline　making　etc.　Significantly,　this　review　refines　a　few　key　tasks　for　modification　of　the　data-driven　approaches　in　the　context　of　application　to　building　energy　analysis.　The　conclusions　drawn　in　this　review　could　facilitate　future　micro-scale　changes　of　energy　use　for　a　particular　building　through　the　appropriate　retrofit　and　the　inclusion　of　renewable　energy　technologies.　It　also　paves　an　avenue　to　explore　potential　in　macro-scale　energy-reduction　with　consideration　of　customer　demands.　All　these　will　be　useful　to　establish　a　better　long-term　strategy　for　urban　sustainability.