In　recent　years,　the　problem　of　energy　consumption　has　received　considerable　attention.　Among　them,　the　proportion　of　energy　consumption　in　public　buildings　accounts　for　a　substantial　part,　and　there　is　a　large　space　for　energy　saving　in　this　field.　Practically,　the　major　limiting　factor　for　energy　consumption　optimization　is　indoor　environment　comfort　degree.　As　such,　a　trade-off　problem　between　energy　conservation　and　indoor　environment　must　be　resolved.　In　this　paper,　an　office　in　a　public　building　is　chosen　as　the　experimental　object,　with　2520　real　observations　collected　for　two　months,　a　series　of　intelligence　based　techniques　are　combined　and　applied　to　deal　with　this　optimization　problem.　First,　Partial　Rank　Correlation　Coefficient　(PRCC)　was　employed　to　perform　correlation　analysis　for　a　set　of　candidate　variables,　such　that　the　computational　time　of　subsequent　model　training　is　reduced　by　8.32　seconds　as　compared　to　the　case　where　no　PRCC　is　in　use.　Following　this,　Genetic　Algorithm　(GA)-based　Levenberg-Marquardt　algorithm　(LM)　are　applied　collectively　to　optimize　the　conventional　Back　Propagation　(BP)　neural　network,　and　results　suggest　that　there　was　a　marked　improvement　in　neural　model　performance,　on　one　hand　the　epoch　number　for　convergence　drops　from　72　to　13,　on　the　other　hand　the　mean　squared　error　for　test　set　is　reduced　by　7.46e-4　(from　9.93e-4　to　2.47e-4).　Finally,　pigeon-inspired　Optimization　(PIO)　algorithm　is　employed　to　address　the　formulated　optimization　problem　and　the　optimal　value　for　Predicted　Mean　Vote　(PMV)　and　Indoor　Air　Quality　(IAQ)　are　-0.4775　and　0.007826,　respectively.　To　verify　the　validity　of　our　results　that　derived　by　the　proposed　approach,　corresponding　parameters　are　applied　in　a　practical　scenario.　The　experimental　result　indicates　that　the　energy　efficiency　is　improved　by　around　18.6%　compared　to　that　yielded　before　optimization　is　performed,　which　demonstrates　the　applicability　and　effectiveness　of　the　proposed　method.