This paper presents a hybrid model framework consisting of a self-organizing map clustering and the Scenario Fan Simulator model for hydropower scheduling. First, the clustering model groups hydro inflow scenarios according to their features, such as trend, distance, and variation. Then, the prominent scenarios are selected from groups to feed into the hydropower scheduling model. The results show how the proposed model is able to conduct a disaggregated level of energy dispatching and deal with the multiple energy generators. Furthermore, the proposed hybrid model can significantly reduce the computational time by 80%, with the same load shedding indications compared to the case without scenario reduction.

Knowing vegetation type in an area is crucial for several applications, including ecology, land use management, and infrastructure risk assessment. In combination with recent advancements in image processing, remote sensing technology has been used to perform fast vegetation type estimation and reduce the need for intensive and time-consuming field-based surveys. This paper proposes a weakly supervised method based on deep learning to estimate tree species relying on multi-spectral high-resolution satellite images. We tested the approach against noisy labels, which often occur in real-world datasets. We validate our approach for a study area in Norway and in Italy using images taken in different periods of the year. Our method significantly enhances the quality of the available forestry inventory dataset.

Disasters Such As Hurricanes, Earthquakes, Wildfires, Etc. Are Felt Most Acutely At local and regional levels. These events have exposed weaknesses in how well-prepared infrastructure operators are to keep their services and provide resilient responses. Outages and service disruptions are largely due to the inability of the affected city infrastructure (i.e., power grids) to cope with random and dynamic disruptive events, translating into resilience deficiencies. A significant challenge is the lack of data availability, sharing, and analysis for emergency planning, and restoration. The Special Section (SS) on Data-driven Reliable and Resilient Energy System Against Disasters in the IEEE Transactions on Industrial Informatics aims to address the data-driven approaches for power systems and infrastructure reliability and resilience during small- and large-scale extreme weather events or natural disasters. The specific aim is to utilize the advancements in data mining and data processing to minimize catastrophic conditions that affect the quality of critical infrastructure operations, quality of life, and economic activities.

During extreme weather events, such as hurricanes, trees can cause significant challenges for the local communities with roadway closures or power outages. Local responders must act quickly with information regarding the extent and severity of hurricane damage to better manage recovery procedures following natural disasters. This article proposes an approach to automatically identify fallen trees on roadways using high-resolution satellite imagery before and after a hurricane. The approach detects fallen trees on roadways via a covoting strategy of three different algorithms and tailored dissimilarity scores. The proposed method does not rely on the large manually labeled satellite image data, making it more practical than existing approaches. Our solution has been implemented and validated on an actual roadway closure dataset from Hurricane Michael in Tallahassee, Florida, in October 2018.

Vegetation is one of the primary causes of outages in electricity transmission and distribution networks and represents a significant expense in maintaining a power grid. While LiDAR or multi-view images can be used for detecting vegetation along power lines, such technologies are costly and difficult to acquire to cover widespread electricity networks. This paper proposes a framework for 3D mapping of trees along power lines using monocular high-resolution satellite images. Such type of imagery has become nowadays affordable and easy to acquire. Furthermore, single snapshots can cover a large portion of the grid in high revisiting time. We train and test different state-of-the-art models to map the contextual information from images into a height prediction. We validate our proposed satellite-based framework for an electricity distribution network in the western part of Norway using actual LiDAR data.

Smart cities can be viewed as large-scale Cyber-Physical Systems (CPS) where different sensors and devices record the cyber and physical indicators of the city systems. The collected data are used for improving urban life by offering services such as accurate electric load forecasting, and more efficient traffic management. Traditional monitoring for electricity and transportation networks generally do not provide full observability due to their limited coverage as well as high implementation and maintenance costs. For example, continuous traffic data collection is mostly limited to major highways only in big cities, whereas local roadways are usually covered once or twice a year. Also, there are no high-fidelity and real-time electric monitoring systems in all parts of power distribution networks. Combining the limited data from each of the urban systems together (e.g., electricity, transportation, environment, etc.) provides a better picture of the energy flow in a city. Furthermore, a city should be considered as a collection of the layers of tangled infrastructure networks, which connects people, places, and resources. Therefore, the study of traffic or electricity consumption forecasting should go beyond the transportation and electricity networks and merge with each other and even with other city networks such as environmental networks. As such, this article proposes a Bayesian spatiotemporal Gaussian Process model that employs the most informative spatiotemporal interdependency among different interconnected networks (in this case, electricity, transportation, and weather). The proposed load forecasting method is compared with other state-of-the-art methods using real-life data obtained from the City of Tallahassee in Florida. Results show that the proposed Bayesian spatiotemporal Gaussian Process model outperforms state-of-the-art methods.

In the research of smart buildings, human activity recognition is an important cornerstone for numerous emerging applications. Although several sensing techniques have been proposed for human activity identification, they require either the user instrumentation or additional infrastructure, that are inconvenient, privacy-intrusive, and expensive. To address these issues, a WiFi-enabled device-free human activity recognition system, namely SmartSense, is proposed in this paper. By upgrading commercial WiFi routers with our designed firmware, fine-grained channel state information (CSI) from PHY layer can be directly extracted from the existing WiFi traffic. In this paper, we propose SmartSense, a device-free human activity recognition system that only leverages existing commercial off-the-shelf WiFi routers. By exploiting the prevalence of WiFi infrastructure in buildings, we developed a novel CSI-enabled Internet of Things platform to collect the CSI measurements from regular data frames. To identify different human activities, a novel machine learning tool, namely multiple kernel semi-representation learning (MKSRL) method is established. MKSRL allows the input of expert domain knowledge in a flexible way and conducts automatic and effective multikernel representation learning for the activity recognition task. Each stage of MKSRL is computationally efficient and theoretically guaranteed, and they can be integrated seamlessly within the reproducing kernel framework for the overall information extraction, representation, and fusion. We conducted experiments to comprehensively evaluate the performance of SmartSense in three common indoor environments. Experimental results validate that SmartSense can provide an activity recognition accuracy of 98%, which achieves significant performance gain over the existing methods.

Urban mobility is a multidimensional characteristic of cities experienced as layers of interconnected infrastructures, places, people, and information. Therefore, the study of networks such as electricity and transportation systems should go beyond an individual network and merge with other networks. This paper proposes the bundled causality engine as a novel information theory-based approach to characterize the causal dependency between flows of electricity and transportation networks. To validate this hypothesis, electricity load forecasting is performed by combining transportation network data with the smart meter data for the City of Tallahassee, FL, USA. The results show a considerable improvement in the short-term load forecasting accuracy at the household level.

In this study we consider the problem of outlier detection with multiple co-evolving time series data. To capture both the temporal dependence and the inter-series relatedness, a multi-task non-parametric model is proposed, which can be extended to data with a broader exponential family distribution by adopting the notion of Bregman divergence. Albeit convex, the learning problem can be hard as the time series accumulate. In this regards, an efficient randomized block coordinate descent (RBCD) algorithm is proposed. The model and the algorithm is tested with a real-world application, involving outlier detection and event analysis in power distribution networks with high resolution multi-stream measurements. It is shown that the incorporation of inter-series relatedness enables the detection of system level events which would otherwise be unobservable with traditional methods.

Smart buildings today are aimed at providing safe, healthy, comfortable, affordable, and beautiful spaces in a carbon and energy-efficient way. They are emerging as complex cyber-physical systems with humans in the loop. Cost, the need to cope with increasing functional complexity, flexibility, fragmentation of the supply chain, and time-to-market pressure are rendering the traditional heuristic and ad hoc design paradigms inefficient and insufficient for the future. In this paper, we present a platform-based methodology for smart building design. Platform-based design (PBD) promotes the reuse of hardware and software on shared infrastructures, enables rapid prototyping of applications, and involves extensive exploration of the design space to optimize design performance. In this paper, we identify, abstract, and formalize components of smart buildings, and present a design flow that maps high-level specifications of desired building applications to their physical implementations under the PBD framework. A case study on the design of on-demand heating, ventilation, and air conditioning (HVAC) systems is presented to demonstrate the use of PBD.