Financial Incentives (FIs) for green buildings are a major component of energy policy planning and play a vital role in the promotion of sustainable development and carbon mitigation strategies. Despite the presence of numerous FIs in Canada, there is still a lack of understanding on their distribution and effectiveness. This review first investigates the FIs available for residential and commercial buildings in Canada, and then performs a comprehensive review of studies related to FIs’ effectiveness evaluation. It is found that FIs for buildings in Canada can be distributed into four categories: tax, loans, grants, and rebates. Among these, rebates from utility providers are the most common and are administered in all provinces. In addition to these, special incentives are available for three end-users (low-income, aboriginal people, landlords and tenants) and for three types of buildings (heritage, non-profit and energy rated). A clear contrast is observed on FIs offered in three regulatory regimes (Federal, provincial and municipal). Four provinces (Alberta, British Columbia, Ontario and Quebec) are leading in green building efforts. The in-depth literature review was also used to develop an understanding on the criteria used in effectiveness evaluation and the factors impacting effectiveness. Based on the findings of different studies on FIs effectiveness, a generic approach for evaluation of FIs is proposed that can help in deploying successful FIs programs. The results of this review are of importance to the policymakers, government authorities, and utilities engaged in designing and improving FIs for energy efficient buildings.
We present a system and study of personalized energyrelated recommendation. AgentSwitch utilizes electricity usage data collected from users' households over a period of time to realize a range of smart energy-related recommendations on energy tariffs, load detection and usage shifting. The web service is driven by a third party real-time energy tariff API (uSwitch), an energy data store, a set of algorithms for usage prediction, and appliance-level load disaggregation. We present the system design and user evaluation consisting of interviews and interface walkthroughs. We recruited participants from a previous study during which three months of their household's energy use was recorded to evaluate personalized recommendations in AgentSwitch. Our contributions are a) a systems architecture for personalized energy services; and b) findings from the evaluation that reveal challenges in designing energy-related recommender systems. In response to the challenges we formulate design recommendations to mitigate barriers to switching tariffs, to incentivize load shifting, and to automate energy management.
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