Occupancy　behaviour　plays　an　important　role　in　energy　consumption　in　buildings.　Currently,　the　shallow　understanding　of　occupancy　has　led　to　a　considerable　performance　gap　between　predicted　and　measured　energy　use.　This　paper　presents　an　approach　to　estimate　the　occupancy　based　on　blind　system　identification　(BSI),　and　a　prediction　model　of　electricity　consumption　by　an　air-conditioning　system　is　developed　and　reported　based　on　an　artificial　neural　network　with　the　BSI　estimation　of　the　number　of　occupants　as　an　input.　This　starts　from　the　identification　of　indoor　CO2　dynamics　derived　from　the　mass-conservation　law　and　venting　levels.　The　unknown　parameters,　including　the　occupancy　and　model　parameters,　are　estimated　by　using　a　frequentist　maximum-likelihood　algorithm　and　Bayesian　estimation.　The　second　phase　is　to　establish　the　prediction　model　of　the　electricity　consumption　of　the　air-conditioning　system　by　using　a　feed-forward　neural　network　(FFNN)　and　extreme　learning　machine　(ELM),　as　well　as　ensemble　models.　To　analyse　some　aspects　of　the　benchmark　test　for　identifying　the　effect　of　structure　parameters　and　input-selection　alternatives,　three　studies　are　conducted　on　(1)　the　effect　of　predictor　selection　based　on　principal　component　analysis,　(2)　the　effect　of　the　estimated　occupancy　as　the　supplementary　input,　and　(3)　the　effect　of　the　neural　network　ensemble.　The　result　shows　that　the　occupancy　number,　as　the　input,　is　able　to　improve　the　accuracy　in　predicting　energy　consumption　using　a　neural　network　model.