The　difficulty　in　balancing　energy　supply　and　demand　is　increasing　due　to　the　growth　of　diversified　and　flexible　building　energy　resources,　particularly　the　rapid　development　of　intermittent　renewable　energy　being　added　into　the　power　grid.　The　accuracy　of　building　energy　consumption　prediction　is　of　top　priority　for　the　electricity　market　management　to　ensure　grid　safety　and　reduce　financial　risks.　The　accuracy　and　speed　of　load　prediction　are　fundamental　prerequisites　for　different　objectives　such　as　long-term　planning　and　short-term　optimization　of　energy　systems　in　buildings　and　the　power　grid.　The　past　few　decades　have　seen　the　impressive　development　of　time　series　load　forecasting　models　focusing　on　different　domains　and　objectives.　This　paper　presents　an　in-depth　review　and　discussion　of　building　energy　prediction　models.　Three　widely　used　prediction　approaches,　namely,　building　physical　energy　models　(i.e.,　white　box),　data-driven　models　(i.e.,　black　box),　and　hybrid　models　(i.e.,　grey　box),　were　classified　and　introduced.　The　principles,　advantages,　limitations,　and　practical　applications　of　each　model　were　investigated.　Based　on　this　review,　the　research　priorities　and　future　directions　in　the　domain　of　building　energy　prediction　are　highlighted.　The　conclusions　drawn　in　this　review　could　guide　the　future　development　of　building　energy　prediction,　and　therefore　facilitate　the　energy　management　and　efficiency　of　buildings.@2022　the　author.　published　by　clsevier　ltd　.　this　is　an　open　access　article　under　the　CC　by　license　(http://creativecommons.org/licenses/by/4.0/)