The　modeling　of　complex　systems　poses　a　great　challenge　due　to　the　multitude　of　unknowns　and　complexity　involved.　Meanwhile,　the　piecewise　linear　(PWL)　model　has　been　proven　to　have　the　ability　to　approximate　complex　nonlinear　systems.　The　construction　of　a　PWL　model　entail　to　partitioning　the　state　space　into　finite　regions,　and　estimating　the　parameters　of　each　subsystem.　In　this　paper,　we　propose　a　novel　approach　to　accomplish　these　two　tasks　via　an　intelligent　Voronoi　partition　approach　and　solving　a　series　of　optimization　problems.　Firstly,　we　estimate　the　certain　range　of　the　subsystem　parameters　of　one　region　directly　from　noisy　data.　This　estimation　is　fulfilled　by　set　operation　between　noise　zonotope　and　the　input-state　data　set,　the　optimal　subsystem　parameters　are　obtained　by　minimizing　the　noise　zonotope.　Secondly,　this　procedure　is　applied　to　all　regions,　a　cost　function　is　designed　based　on　these　noise　zonotopes,　by　changing　the　partition,　the　cost　function　varies.　Finally,　an　annealing　simulation　(SA)　algorithm　is　utilized　to　search　for　the　most　adequate　partition　to　ensure　the　fitting　accuracy.　The　effectiveness　of　the　proposed　method　is　demonstrated　through　a　numerical　example　and　on　experimental　data　from　an　actual　water　supply　system.