Towards human-like conversational search systems

Voice search is currently widely available on the majority of mobile devices via use of Virtual Personal Assistants. However, despite its general availability, the use of voice interaction remains sporadic and is limited to basic search tasks such as checking weather updates and looking up answers to factual queries. Present-day voice search systems struggle to use relevant contextual information to maintain conversational state, and lack conversational initiative needed to clarify user’s intent, which hampers their usability and prevents users from engaging in more complex interaction activities. This research investigates the potential of a hypothesised interactive information retrieval system with human-like conversational abilities. To this end, we propose a series of usability studies that involve a working prototype of a conversational system that uses real time speech synthesis. The proposed experiments seek to provide empirical evidence that enabling a voice search system with human-like conversational abilities can lead to increased likelihood of its adoption

Efficient use of deep learning and machine learning for load forecasting in South African power distribution networks

Abstract: Load forecasting, which is the act of anticipating future loads, has been shown to be important in power system network planning, operations and maintenance. Artificial Intelligence (AI) techniques have been shown to be good tools for load forecasting. Load forecasting can assist power distribution utilities maximise their revenue through optimising maintenance planning. With the dawn of the smart grid, first world countries have moved past the customer’s point of supply and use smart meters to forecast customer loads. These recent studies also utilise recent state of the art AI techniques such as deep learning techniques. Weather parameters are such as temperature, humidity and rainfall are usually used as parameters in these studies. South African load forecasting studies are outdated and recent studies are limited. Most of these studies are from 2010, and dating backwards to 1999. Hence they do not use recent state of the art AI techniques. The studies do not focus at distribution level load forecasting for optimal maintenance planning. The impact of adjusting power consumption data when there are spikes and dips in the data was not investigated in all these South African studies. These studies did not investigate the impact of weather parameters on different South African loads and hence load forecasting performance...D.Phil. (Electrical and Electronic Management