A study and a directory of energy consumption data sets of buildings

Energy consumption data are required to perform analysis, modelling, evaluation, and optimisation of energy usage in buildings. While a variety of energy consumption data sets have been examined and reported in the literature, there is a lack of a comprehensive categorisation and analysis of the available data sets. In this study, an overview of energy consumption data of buildings is provided. Three common strategies for generating energy consumption data, i.e., measurement, survey, and simulation, are described. A number of important characteristics pertaining to each strategy and the resulting data sets are discussed. In addition, a directory of energy consumption data sets of buildings is developed. The data sets are collected from either published papers or energy related organisations. The main contributions of this study include establishing a resource pertaining to energy consumption data sets and providing information related to the characteristics and availability of the respective data sets; therefore facilitating and promoting research activities in energy consumption data analysis.

Examining the perception of tenants in sustainable office buildings

Purpose – The purpose of this paper is to address two major challenges faced by sustainable building owners: first, address the gap between an occupant’s expectations of sustainable building outcomes and what the building actually provides and second, overcome the lack of user knowledge about sustainability design and operation for a particular with regards to performance. Design/methodology/approach – This study used a focus group approach to investigate the gap between: user expectations and sustainable building performance. The study surveyed occupants of sustainable office buildings in Melbourne, Australia. Findings – There is no significant relationship between users’ expectations and users’ experience of sustainable building performance and users’ knowledge about sustainability and the building they were worked in. Research limitations/implications – The research was limited to sustainable office buildings. New office buildings seeking to incorporate sustainability which need to focus on the needs of tenants in order to maximise value. Practical implications – There is an urgent need to ensure sustainable office buildings meet the needs of present and future occupiers without compromising short and long-term occupier satisfaction levels with regards to sustainability and operation of the building. Social implications – Increasing the level of sustainability in office buildings has been a major trend over the past decade however the tenants need to be consulted in the post-occupancy phase. Originality/value – Little attention has been given in the property management literature to sustainable office buildings and value drivers. This is an original and innovative study, partly due to the recent developments in sustainable buildings.

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a result, high forecast accuracy is required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment of power grid. Artificial Intelligence (AI) based techniques are being developed and deployed worldwide in on Varity of applications, because of its superior capability to handle the complex input and output relationship. This paper provides the comprehensive and systematic literature review of Artificial Intelligence based short term load forecasting techniques. The major objective of this study is to review, identify, evaluate and analyze the performance of Artificial Intelligence (AI) based load forecast models and research gaps. The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. Published literature presented in this paper show the potential of AI techniques for effective load forecasting in order to achieve the concept of smart grid and buildings.