The　consumption　of　energy　in　buildings　has　risen　abruptly　over　the　last　decades.　Due　to　less　energy-efficient　buildings,　most　of　the　energy　is　being　thrown　in　our　surroundings　thus　making　an　adverse　effect　on　our　environment.　In　this　paper,　heating　and　cooling　loads　of　private　or　non-commercial　buildings　are　covered.　By　implementing　the　proposed　technique,　which　is　a　blend　of　cluster　analysis　and　Artificial　Neural　Network　(ANN),　evaluation　and　prediction　are　performed.　The　estimation　of　heating　and　cooling　loads　of　private　or　non-commercial　buildings　are　performed　using　eight　input　variables　in　the　ANN-based　model.　The　details　of　variables　are　as　follows,　a　relative　surface　area,　total　height,　compactness,　roof　area,　glazing　area　distribution,　orientation,　glazing　area,　and　wall　area.　K-means　clustering　methodology　is　then　used　to　cluster　buildings　on　the　basis　of　output　variables.　Stand　on　simulated　literature　data,　evaluation　of　768　different　private　or　non-commercial　buildings　is　done　using　the　above-suggested　method.　Research　results　depicted　that　depending　upon　input　variables,　the　above-suggested　approach　can　efficiently　evaluate　heating　and　cooling　load　that　is　very　much　close　to　real　test　results.　©　2020　IEEE.