Predicting large scale fine grain energy consumption

Today a large volume of energy-related data have been continuously collected. Extracting actionable knowledge from such data is a multi-step process that opens up a variety of interesting and novel research issues across two domains: energy and computer science. The computer science aim is to provide energy scientists with cutting-edge and scalable engines to effectively support them in their daily research activities. This paper presents SPEC, a scalable and distributed predictor of fine grain energy consumption in buildings. SPEC exploits a data stream methodology analysis over a sliding time window to train a prediction model tailored to each building. The building model is then exploited to predict the upcoming energy consumption at a time instant in the near future. SPEC currently integrates the artificial neural networks technique and the random forest regression algorithm. The SPEC methodology exploits the computational advantages of distributed computing frameworks as the current implementation runs on Spark. As a case study, real data of thermal energy consumption collected in a major city have been exploited to preliminarily assess the SPEC accuracy. The initial results are promising and represent a first step towards predicting fine grain energy consumption over a sliding time window

An investigation into the depiction of smart grid technology

Increasing climate change concerns and depletion of fossil fuels demand greater efficiency in electricity production and consumption. Smart Grid is a vision of an enhanced electricity grid that integrates the electric grid with communication and sensing technologies to improve energy delivery. A number of initiatives have been embarked upon to reach this vision. Databases of Smart Grid projects are being kept to hallmark the state of development and advise future project design. However, to date, there is no method of comparing projects‟ results. This means that it is difficult to identify the most successful projects. In addition, details of projects tend to be descriptive and there is no standard method of representing Smart Grid systems. The first Smart Grid technologies are about to be deployed in homes, and yet, there are little research examining how domestic consumers would react to a full set of Smart Grid technology. This is important because the opinions and participation of domestic consumers could lead to the success or failure of the Smart Grid system. This research aims to device a representation system that enables the comparison of smart grid technology available for the residential consumers in the UK. The objectives are to: (i) review and identify existing representations of home Smart Grid technology; (ii) review and identify the general system representation methods; (iii) develop a representation method that maps and enables the comparison of Smart Grid technology in homes; (iv) validate the design of the representation method with relevant stakeholders. Through a four step methodology these objectives were achieved. Thirty Smart Grid diagrams taken from journals and conference papers were analysed and categorised into five groups based of the type of communication features they contained. The results from this analysis guided the development of a Smart Grid representation method. Two Smart Grid systems that are available on the market were depicted using the representation method and were used to validate the design through interviewing 10 residential electricity consumers. As an outcome, this research had delivered a validated representation method that could be used to depict electricity management systems. It could be adopted by energy companies to convey the functions and benefits of Smart Grid technologies to potential customers