Buildings　emit　a　great　deal　of　carbon　dioxide　and　use　a　lot　of　energy.　The　study　of　building　energy　consumption　is　useful　for　the　sustainable　development　of　multi-energy　planning　and　energy-saving　strategies.　Therefore,　a　sustainable　development　for　building　energy　consumption　based　on　the　improved　rafflesia　optimization　algorithm　(ROA)　with　feature　selection　and　ensemble　deep　learning　is　proposed　in　this　paper.　This　method　can　explore　data　on　building　energy　usage,　assess　prediction　accuracy,　and　address　concerns　that　building　energy　usage　research　must　address.　The　proposed　model　first　uses　an　improved　self-organizing　map　with　a　new　neighborhood　function　to　select　important　features.　After　that,　it　uses　ensemble　deep　learning　to　accurately　anticipate　the　building＇s　energy　usage.　In　addition,　the　improved　ROA　is　used　to　fine-tune　parameters　for　feature　selection　and　ensemble　deep　learning.　This　research　uses　the　dataset　of　the　American　Society　of　Heating,　Refrigerating,　and　Air　Conditioning　Engineers　(ASHRAE)　to　compare　the　performance　of　several　modeling　approaches.　It　identifies　the　top　five　most　important　features　based　on　the　model＇s　results.　Furthermore,　the　proposed　model　can　be　successfully　applied　to　a　real-world　application.　They　both　have　the　lowest　root　mean　squared　errors　among　the　approaches　examined.　The　proposed　model　indeed　provides　the　benefits　of　feature　selection　and　ensemble　deep　learning　with　the　improved　ROA　for　the　prediction　of　building　energy　consumption.