Natural language processing (NLP) is a subfield of linguistics, computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data.

Challenges in natural language processing frequently involve speech recognition, natural language understanding, and natural language generation.

The Eco-Bot project will classify already existing socioeconomic energy consumer models derived by European and international research projects, activities and recommendations, and will produce a taxonomy of those models while mapping them to actual market segments. Eco-Bot will build its engagement strategies on careful analysis of socio-economic, demographic and cultural factors, including market segmentation. This will provide a thorough definition of the target groups to be selected for the purposes of the project, with an emphasis on the demographics in the use cases.

Monitoring energy consumption of individual appliances using individual appliance sensors or submetering in a house or a commercial building is often impractical and expensive, especially since the number of loads in residential and commercial buildings is rapidly increasing. On the other hand, energy disaggregation via Non-Intrusive Load Monitoring (NILM) offers a non-intrusive, purely computational, software-based approach to separate aggregate load obtained from a single electricity meter into individual appliance loads. Researchers at Strathclyde will focus on tackling the challenges of NILM algorithms suitable for low rate (1-60s) to very low rate (15-60mins) disaggregation of smart meter data.