Photovoltaic　(PV)　power　generation　is　widely　utilized　to　satisfy　the　increasing　energy　demand　due　to　its　cleanness　and　inexhaustibility.　Accurate　PV　power　forecasting　can　improve　the　penetration　of　PV　power　in　the　grid.　However,　it　is　pretty　challenging　to　predict　PV　power　in　short-term　under　precious　future　meteorological　information　absence　conditions.　To　address　this　problem,　this　study　proposes　the　hybrid　Contrastive　Learning　and　Temporal　Convolutional　Network　(CL-TCN),　and　this　forecasting　approach　consists　of　two　parts,　including　model　training　and　adaptive　processes　of　forecasting　models.　In　the　model　training　stage,　this　forecasting　method　firstly　trains　18　TCN　models　for　18　time　points　from　9:00　a.m.　to　17:30　p.m.　These　TCN　models　are　trained　by　only　using　historical　PV　power　data　samples,　and　each　model　is　used　to　predict　the　next　half-hour　power　output.　The　adaptive　process　of　models　means　that,　in　a　practical　forecasting　stage,　PV　power　samples　from　historical　data　are　firstly　evaluated　and　scored　by　a　CL　based　data　scoring　mechanism　to　search　for　the　most　similar　data　samples　to　current　measured　samples.　Then　these　similar　samples　are　further　applied　to　training　a　single　above-mentioned　well-trained　TCN　model　to　improve　its　performance　in　forecasting　the　next　half-hour　PV　power.　The　experimental　results　tested　at　the　time　resolution　of　30　min　demonstrate　that　the　proposed　approach　has　superior　performance　in　forecasting　accuracy　not　only　in　smooth　PV　power　samples　but　also　in　fluctuating　PV　power　samples.　Moreover,　the　proposed　CL　based　data　scoring　mechanism　can　filter　useless　data　samples　effectively　accelerating　the　forecasting　process.