NEAPOLI's Director, invited speaker for a technical presentation on Machine Learning

Energy efficiency is an important part of our life for the current and future society. The world energy consumption is increasing rapidly and in particular, Malaysian residential and commercial sector energy consumption has almost doubled from 3800 ktoe (year 2000) to 7600 ktoe (year 2014). The key reasons behind such an increase is the growth in population, greater demand for building services, the need for better comfort levels, hot and tropical weather conditions and longer duration of occupants spending time inside buildings. In fact, maintaining a good thermal comfort is important for the occupants in the buildings in order to achieve an efficient working and production activity. For this reason, improving energy efficiency in buildings is a prime objective of the Malaysian government as well as global policy makers.

Typically, Malaysian commercial buildings consumes high amount of energy which contributes to higher carbon emissions. Most of these energy demand in the buildings are satisfied with the high quality or high exergy sources (e.g. fossil fuels). Recently, Internet of Things (IoT) technology with power over Ethernet and artificial intelligence (AI) can offer disruptive opportunities in revolutionizing the building energy management.

Therefore, the main objective of this seminar is to expose on:

  1. Advance in data analytics and machine learning – investigating data driven building energy consumption prediction models to achieve maximum energy savings
  2. Case studies of machine learning models to predict cooling energy consumption of a building on several different design parameters
  3. Compare the prediction performance and computational efficiency of the model against measurement or energy simulation data
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