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Energy efficiency is a complex topic to integrate into higher education curricula, with limited success internationally or in Australia. This paper discusses one of the successful initiatives within the Energy Efficiency Training Program , which was jointly managed and implemented by the New South Wales Office of Environment and Heritage and Department of Education and Communities. The state government initiative aimed to increase the knowledge and skills of the New South Wales workforce, help business to identify and implement energy efficiency projects, and provide professional development for the training providers. Key sectors targeted included property, construction, manufacturing and services. The Program was externally evaluated over the three years 2011–2013 and a range of insights were gained through these facilitated reflective opportunities, confirming and building upon literature on the topic to date. This paper presents lessons learned from the engineering part of the program (‘the project’), spanning government agencies, academic institutions, and academia. The paper begins with a contextual summary, followed by a synthesis of key learnings and implications for future training initiatives. It is intended that sharing these lessons will contribute to literature in the field, and assist other organisations in Australia and overseas planning similar initiatives. [ABSTRACT FROM AUTHOR]

This study presents a new strategy using cluster analysis, Cubist regression models and Particle Swarm Optimization to forecast next-day total electricity usage and peak electricity demand of a building portfolio. Cluster analysis with a combined dissimilarity measure was first used to group daily electricity usage profiles of the building portfolio. The clustering result was then considered in the training of the Cubist-based forecasting models in order to improve the forecasting accuracy. A Particle Swarm Optimization algorithm was used to determine the optimal parameters in the cluster analysis to further improve the forecasting accuracy. The performance of this strategy was evaluated using the electricity usage data of 40 university buildings. The results showed that the difference between the measured and predicted daily total electricity usage was 4.7% in terms of the coefficient of variation of the root-mean-squared error (CV(RMSE)) and 3.3% in terms of mean absolute percentage error (MAPE) and the difference between the measured and predicted daily peak load was 6.0% in CV(RMSE) and 5.3% in MAPE. The proposed strategy can effectively improve the accuracy of the forecasting result by up to 18.1% and 12.2% when compared to the strategy which did not consider the clustering result of the daily electricity usage profiles in the forecasting models and the strategy which considered the clustering result obtained using a single dissimilarity measure. Compared to the mean level of the nine strategies that used different regression methods, the proposed strategy can improve the forecasting accuracy by up to 42.2%. The results of this study can be further used to assist in the development of building optimal control and operation strategies. • A strategy was developed for electricity usage forecasting of campus buildings. • Clustering with a combined distance was used to improve forecasting performance. • Parameters in the clustering were optimized using Particle Swarm Optimization. • Cubist regression models were used to increase forecasting accuracy. • This strategy outperformed other electricity usage forecasting strategies compared. [ABSTRACT FROM AUTHOR]