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Semantics associates meaning with Internet of Things (IoT) data and facilitates the development of intelligent IoT applications and services. However, the big volume of the data generated by IoT devices and resource limitations of these devices have given rise to challenges for applying semantic technologies. In this article, we present Cloud and edge based IoT architectures for semantic reasoning. We report three experiments that demonstrate how edge computing can facilitate IoT systems in terms of data transfer and semantic reasoning. We also analyze how distributing reasoning tasks between the Cloud and edge devices affects system performance.

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This paper presents a novel deep learning architecture for short-term load forecasting of building energy loads. The architecture is based on a simple base learner and multiple boosting systems that are modelled as a single deep neural network. The architecture transforms the original multivariate time series into multiple cascading univariate time series. Together with sparse interactions, parameter sharing and equivariant representations, this approach makes it possible to combat against overfitting while still achieving good presentation power with a deep network architecture. The architecture is evaluated in several short-term load forecasting tasks with energy data from an office building in Finland. The proposed architecture outperforms state-of-the-art load forecasting model in all the tasks.

Demand-side flexibility management is a key enabler of the transformation towards the high penetration of renewable energy resources. We present a flexibility-management system called Flex4Grid, which is designed to provide a low-cost solution for residential consumers wishing to participate in power-grid balancing. The Flex4Grid system continuously forecasts the need for flexibility in a power grid and informs consumers about the flexibility-management periods. Consumers can provide their flexibility to an aggregator in exchange for a reward, which depends on the selected incentive scheme. The automation of the flexibility-management events is provided by interfacing with devices and the system via the Z-Wave and open platform communication unified architecture (OPC UA) technologies. The Flex4Grid system has been deployed in three pilots in Slovenia and Germany. A large-scale pilot in Celje, Slovenia, with 1047 participants, was used to collect statistical data regarding how consumers participate in the flexibility-management events. A critical peak-pricing incentive scheme was used in the Celje pilot. The smaller German pilots with a total of 185 participants were used for testing the technical capabilities of the system. User-satisfaction surveys were performed in all three pilots. The results indicate that the proposed approach is appropriate for engaging consumers in flexibility-management events. On average, the pilots' participants reduced their load by 10% during a peak event. The overall scores of the user-satisfaction survey were 3.4 and 3.9 on a 5-point Likert scale for the German and Slovenian pilots, respectively. These are good results for a prototype system; however, improvements to the stability and usability of the system are required.