The　prediction　of　appliances　energy　consumption　in　building　belongs　to　time　series　forecasting　problem,　which　can　be　solved　by　echo　state　network　(ESN).　However,　due　to　the　randomly　initialized　inputs　and　reservoir,　some　redundant　or　irrelevant　components　are　inevitably　generated　in　original　ESN.　To　solve　this　problem,　the　adaptive　sparse　deep　echo　state　network　(ASDESN)　is　proposed,　in　which　the　information　is　processed　layer　by　layer.　Firstly,　the　principal　component　analysis　(PCA)　layer　is　inserted　to　penalize　the　redundant　projection　transmitted　between　sub-reservoirs.　Secondly,　the　coordinate　descent　based　adaptive　sparse　learning　method　is　proposed　to　generate　the　sparse　output　weights.　Particularly,　the　designed　adaptive　threshold　strategy　is　able　to　enlarge　the　sparsity　of　output　weights　as　network　depth　increases.　Moreover,　the　echo　state　property　(ESP)　of　ASDESN　is　given　to　ensure　its　applications.　The　experiment　results　in　both　simulated　benchmark　and　real　appliances　energy　datasets　illustrate　that　the　proposed　ASDESN　outperforms　other　ESNs　with　higher　prediction　accuracy　and　stability.