Investigating Preconditions for Sustainable Renewable Energy Product–Service Systems in Retail Electricity Markets

Energy transitions are complex and involve interrelated changes in the socio-technical dimensions of society. One major barrier to renewable energy transitions is lock-in from the incumbent socio-technical regime. This study evaluates Energy Product–Service Systems (EPSS) as a renewable energy market mechanism. EPSS offer electricity service performance instead of energy products and appliances for household consumers. Through consumers buying the service, the provider company is enabled to choose, manage and control electrical appliances for best-matched service delivery. Given the heterogenous market players and future uncertainties, this study aims to identify the necessary conditions to achieve a sustainable renewable energy market. Simulation-Based Design for EPSS framework is implemented to assess various hypothetical market conditions’ impact on market efficiency in the short term and long term. The results reveal the specific market characteristics that have a higher chance of causing unexpected results. Ultimately, this paper demonstrates the advantage of implementing Simulation-Based Design for EPSS to design retail electricity markets for renewable energy under competing market mechanisms with heterogenous economic agents

Learning over Knowledge-Base Embeddings for Recommendation

State-of-the-art recommendation algorithms -- especially the collaborative filtering (CF) based approaches with shallow or deep models -- usually work with various unstructured information sources for recommendation, such as textual reviews, visual images, and various implicit or explicit feedbacks. Though structured knowledge bases were considered in content-based approaches, they have been largely neglected recently due to the availability of vast amount of data, and the learning power of many complex models. However, structured knowledge bases exhibit unique advantages in personalized recommendation systems. When the explicit knowledge about users and items is considered for recommendation, the system could provide highly customized recommendations based on users' historical behaviors. A great challenge for using knowledge bases for recommendation is how to integrated large-scale structured and unstructured data, while taking advantage of collaborative filtering for highly accurate performance. Recent achievements on knowledge base embedding sheds light on this problem, which makes it possible to learn user and item representations while preserving the structure of their relationship with external knowledge. In this work, we propose to reason over knowledge base embeddings for personalized recommendation. Specifically, we propose a knowledge base representation learning approach to embed heterogeneous entities for recommendation. Experimental results on real-world dataset verified the superior performance of our approach compared with state-of-the-art baselines

Interoperability of Information Systems and Heterogenous Databases Using XML

Interoperabilily of information systerrrs is the most critical issue facing businesse! that need to access information from multiple idormution systems on tlifferent environments ancl diverse platforms. Interoperability has been a basic requirement for the modern information systems in a competitive and volatile business environment, particularly with the advent of distributed network system and the growing relevance of inter-network communications. Our objective in tltis paper is to develop a comprehensiveframework tofacilitate interoperability smong distributed and heterogeneous information systems and to develop prototype software to validate tlte application of XML in interoperability of infurmation systems and databases