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We propose a novel family of test statistics to detect the presence ofchangepoints in a sequence of dependent, possibly multivariate,functional-valued observations. Our approach allows to test for a very generalclass of changepoints, including the "classical" case of changes in the mean,and even changes in the whole distribution. Our statistics are based on ageneralisation of the empirical energy distance; we propose weightedfunctionals of the energy distance process, which are designed in order toenhance the ability to detect breaks occurring at sample endpoints. Thelimiting distribution of the maximally selected version of our statisticsrequires only the computation of the eigenvalues of the covariance function,thus being readily implementable in the most commonly employed packages, e.g.R. We show that, under the alternative, our statistics are able to detectchangepoints occurring even very close to the beginning/end of the sample. Inthe presence of multiple changepoints, we propose a binary segmentationalgorithm to estimate the number of breaks and the locations thereof.Simulations show that our procedures work very well in finite samples. Wecomplement our theory with applications to financial and temperature data.

On changepoint detection in functional data using empirical energy distance