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Although deep learning is increasingly promising in the field of Non‐Intrusive Load Monitoring (NILM) these days, the high costs of data recording and labelling represent a significant challenge for the training of supervised models. To address this, a cost‐effective sequence‐to‐points NILM solution is proposed, integrating three‐point labelling with non‐causal convolution techniques. The approach introduces a semi‐automatic labelling framework for obtaining NILM three‐point data, which provides a low‐cost data collection and labelling solution for large‐scale applications. Then, a novel loss function combining coordinate loss and confidence loss is developed to address the positional misalignment and negative sample confusion in sequence‐to‐points scenario in NILM. Furthermore, an advanced neural network architecture based on multi‐scale non‐causal temporal convolution techniques is designed to capture unique features and operational modes of different appliances. Experimental results on the UK‐DALE dataset show that the proposed mixed loss function has an advantage over plain Mean Absolute Error (MAE) on the sequence‐to‐points occasion, and the novel network outperforms on all of the appliances, demonstrating its potential for practical NILM applications. This work introduces a low‐cost semi‐automatic labelling framework, significantly reducing the barriers to large‐scale NILM applications. Furthermore, a novel combined coordinate and confidence loss function is proposed, targeting key issues in three‐point regression scenarios and enhancing model precision. Complementing these methodological advancements, our unique MSNCTCN network architecture outperforms existing models, adeptly handling diverse appliance features and working modes.