Energy　efficiency　and　sustainable　development　have　been　the　focus　of　the　world＇s　attention.　In　order　to　promote　the　execution　of　energy　reduction,　energy　control　systems,　which　could　operate　the　electrical　appliances,　are　under　research　at　present.　Before　putting　the　energy　control　systems　into　real　buildings,　comfort　assessment　and　energy　consumption　analysis　need　to　be　conducted　but　such　operations　require　a　large　number　of　test　cases　to　ensure　the　stability　and　effectiveness　of　the　systems.　Nevertheless,　real　data　collection　from　each　building　is　tedious　and　expensive;　and　manual　test　data　generation　may　drop　some　important　effective　factors　or　relationships.　Therefore,　a　tool　of　test　data　generation,　which　could　generate　large　volumes　of　test　data,　is　desperately　needed.　In　this　paper,　we　propose　a　neural　network　model　to　generate　a　large　test　data　set　for　comfort　assessment　and　energy　consumption　analysis.　This　approach　is　based　on　an　existing　set　of　real-world　data,　and　generalizes　it　into　a　larger　data　set.　Our　analysis　indicates　that　the　proposed　approach　is　reliable　and　effective.