Typical Non-Intrusive Load Monitoring (NILM) systems rely on a central meter reading to real-time establish the energy usage of individual appliances within a household or site. This paper introduces a novel simple NILM technique based on a Look-up-Table for achieving with NILM disaggregation. Unlike standard NILM algorithms, which rely on a traditional solver, this innovative Look-up Table algorithm depends entirely on information about each appliance’s average power consumption. It stands out for its simplicity, enabling immediate implementation without necessitating previous system training. The experimental evaluations were conducted using reputable datasets such as Electricity consumption and occupancy (ECO), and Almanac of Minutely Power dataset (AMPDs). As well as a new dataset built from experimental power measuring node in a typical apartment. The suggested approach delivers commendable performance regarding standard evaluation metrics, especially when comparing the scores to other algorithms based long short-term memory (LSTM) neural and multi-objective (MO) optimization algorithms on publicly available datasets. This research offers a strong candidate to the list of algorithms for real-time NILM applications.