  Machine learning models offer the capability to forecast future energy
production or consumption and infer essential unknown variables from existing
data. However, legal and policy constraints within specific energy sectors
render the data sensitive, presenting technical hurdles in utilizing data from
diverse sources. Therefore, we propose adopting a Swarm Learning (SL) scheme,
which replaces the centralized server with a blockchain-based distributed
network to address the security and privacy issues inherent in Federated
Learning (FL)'s centralized architecture. Within this distributed Collaborative
Learning framework, each participating organization governs nodes for
inter-organizational communication. Devices from various organizations utilize
smart contracts for parameter uploading and retrieval. Consensus mechanism
ensures distributed consistency throughout the learning process, guarantees the
transparent trustworthiness and immutability of parameters on-chain. The
efficacy of the proposed framework is substantiated across three real-world
energy series modeling scenarios with superior performance compared to Local
Learning approaches, simultaneously emphasizing enhanced data security and
privacy over Centralized Learning and FL method. Notably, as the number of data
volume and the count of local epochs increases within a threshold, there is an
improvement in model performance accompanied by a reduction in the variance of
performance errors. Consequently, this leads to an increased stability and
reliability in the outcomes produced by the model.
