We investigate forward-backward splitting algorithm of penalty type with inertial effects for finding the zeros of the sum of a maximally monotone operator and a cocoercive one and the convex normal cone to the set of zeroes of an another cocoercive operator. Weak ergodic convergence is obtained for the iterates, provided that a condition express via the Fitzpatrick function of the operator describing the underlying set of the normal cone is verified. Under strong monotonicity assumptions, strong convergence for the sequence of generated iterates can be proved. As a particular instance we consider a convex bilevel minimization problems including the sum of a nonsmooth and a smooth function in the upper level and another smooth function in the lower level. We show that in this context weak nonergodic and strong convergence can be also achieved under inf-compactness assumptions for the involved functions.