Human-in-the-loop energy flexibility integration on a neighbourhood level: Small and Big Data management

Modern buildings provide an enormous amount of data available from various sources ranging from modular wireless sensors to smart meters. As well as enhancing energy management and building performance, the analysis of these datasets can enhance the management of decentralized energy systems (electrical storage, PV generation, heat storage, etc.). To optimize the interaction between the building and the grid, it is essential to determine the total energy flexibility of the user and the building. A building has different possibilities for demand side management, energy storage and energy exchange for which a functional-layered approach is proposed from the user up to building and its interaction with the energy infrastructure. Central is the principle of the human-in-the-loop, where a bottom-up approach places the human needs as a central starting point for the energy interaction optimisation. The combination of Big Data with deep learning techniques offers new possibilities in the prediction of energy use and decentralized renewable energy production (e.g. from local weather data taking into account local phenomena such as urban heat islands). This combined with a more bottom-up approach of multi-agent systems with a gossip-based cooperative approach using Small Data offers decentralized control and monitoring autonomy to reduce the complexity of the energy system integration and transition. This makes it possible to relate the outcomes of the urban energy system integration on a neighbourhood level. The approach is being applied to a typically medium-sized office building. A first application of the human-in-the-loop controlling the lighting systems in the open-plan workplace of the test-bed office building showed some estimated annual energy saving of around 24%. Practical application : Analysis of a large database containing so called Big Data of clusters of buildings seems promising. Therefor there is the need to study the potential impact of utilization of big building operational data in building services industry. Besides this there is also a need for a data mining-based method for analyzing massive building operational data of a specific building, Small Data. This work sets out a general framework and method for doing both and to combine the strength of both approaches. The presented combined approach and results will be of interest to engineers and facility managers wondering what the key constraints to optimal use data to optimize low energy/carbon control strategies might have within their work.