A Demand-Response System for Sustainable Manufacturing Using Linked Data and Machine Learning

Abstract

The spread of demand-response (DR) programs in Europe is a slow but steady process to optimize the use of renewable energy in different sectors including manufacturing. A demand-response program promotes changes of electricity consumption patterns at the end consumer side to match the availability of renewable energy sources through price changes or incentives. This research develops a system that aims to engage manufacturing power consumers through price- and incentive-based DR programs. The system works on data from heterogeneous systems at both supply and demand sides, which are linked through a semantic middleware, instead of centralized data integration. An ontology is used as the integration information model of the semantic middleware. This chapter explains the concept of constructing the ontology by utilizing relational database to ontology mapping techniques, reusing existing ontologies such as OpenADR, SSN, SAREF, etc., and applying ontology alignment methods. Machine learning approaches are developed to forecast both the power generated from renewable energy sources and the power demanded by manufacturing consumers based on their processes. The forecasts are the groundworks to calculate the dynamic electricity price introduced for the DR program. This chapter presents different neural network architectures and compares the experiment results. We compare the results of Deep Neural Network (DNN), Long Short-Term Memory Network (LSTM), Convolutional Neural Network (CNN), and Hybrid architectures. This chapter focuses on the initial phase of the research where we focus on the ontology development method and machine learning experiments using power generation datasets.

A semantic web approach to uplift decentralized household energy data

“…For instance, Gomes et al [45] published micro grid data as Linked Data to enable the examination of new services and algorithms for the administration of micro grids with the inclusion of interoperable data from various grid systems. Similarly, Wicaksono et al [46] incorporated multivariate DR (demand-response) data leveraging Linked Data and ontologies technologies to enhance the prediction results for machine learning applications. Another noticeable advantage of Linked Data principles is that federated SPARQL (SPARQL Query Language for RDF) queries can be performed on such a huge global database on the Web in order to acquire resources that are not available in fixed databases [47].…”

Artificial Intelligence-Based Demand-Side Response Management of Renewable Energy

“…Wicaksono et al create a system that uses pricing and incentive-based DR programmes to engage manufacturing power CUs [25]. Instead of centralized data integration, the system uses data from heterogeneous systems on both the D/S sides, which are linked by semantic middleware.…”