http://wpage.unina.it/valerio.persico/pubs/CloudIoT_FGCS.pdf

Integration of Cloud computing and Internet of Things

Recently, Wireless Sensor Networks (WSNs) have attracted lot of attention due to their pervasive nature and their wide deployment in Internet of Things, Cyber Physical Systems, and other emerging areas. The limited energy associated with WSNs is a major bottleneck of WSN technologies. To overcome this major limitation, the design and development of efficient and high performance energy harvesting systems for WSN environments are being explored. We present a comprehensive taxonomy of the various energy harvesting sources that can be used by WSNs. We also discuss various recently proposed energy prediction models that have the potential to maximize the energy harvested in WSNs. Finally, we identify some of the challenges that still need to be addressed to develop cost-effective, efficient, and reliable energy harvesting systems for the WSN environment.

Energy harvesting in wireless sensor networks: A comprehensive review

Microgrids are subsystems of the distribution grid, which comprises generation capacities, storage devices, and controllable loads, operating as a single controllable system either connected or isolated from the utility grid. In this paper, we present a study on applying a model predictive control approach to the problem of efficiently optimizing microgrid operations while satisfying a time-varying request and operation constraints. The overall problem is formulated using mixed-integer linear programming (MILP), which can be solved in an efficient way by using commercial solvers without resorting to complex heuristics or decompositions techniques. Then, the MILP formulation leads to significant improvements in solution quality and computational burden. A case study of a microgrid is employed to assess the performance of the online optimization-based control strategy and the simulation results are discussed. The method is applied to an experimental microgrid located in Athens, Greece. The experimental results show the feasibility and the effectiveness of the proposed approach.

/pdf/a-model-predictive-control-approach-to-microgrid-operation-q6s5cb0thi.pdf

A Model Predictive Control Approach to Microgrid Operation Optimization

Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM

Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.

Tackling Climate Change with Machine Learning

A key goal of smart grid initiatives is significantly increasing the fraction of grid energy contributed by renewables. One challenge with integrating renewables into the grid is that their power generation is intermittent and uncontrollable. Thus, predicting future renewable generation is important, since the grid must dispatch generators to satisfy demand as generation varies. While manually developing sophisticated prediction models may be feasible for large-scale solar farms, developing them for distributed generation at millions of homes throughout the grid is a challenging problem. To address the problem, in this paper, we explore automatically creating site-specific prediction models for solar power generation from National Weather Service (NWS) weather forecasts using machine learning techniques. We compare multiple regression techniques for generating prediction models, including linear least squares and support vector machines using multiple kernel functions. We evaluate the accuracy of each model using historical NWS forecasts and solar intensity readings from a weather station deployment for nearly a year. Our results show that SVM-based prediction models built using seven distinct weather forecast metrics are 27% more accurate for our site than existing forecast-based models.

/pdf/predicting-solar-generation-from-weather-forecasts-using-1trfvklmbf.pdf

Predicting solar generation from weather forecasts using machine learning

Reducing the energy footprint of data centers continues to receive significant attention due to both its financial and environmental impact. There are numerous methods that limit the impact of both factors, such as expanding the use of renewable energy or participating in automated demand-response programs. To take advantage of these methods, servers and applications must gracefully handle intermittent constraints in their power supply. In this paper, we propose blinking---metered transitions between a high-power active state and a low-power inactive state---as the primary abstraction for conforming to intermittent power constraints. We design Blink, an application-independent hardware-software platform for developing and evaluating blinking applications, and define multiple types of blinking policies. We then use Blink to design BlinkCache, a blinking version of memcached, to demonstrate the effect of blinking on an example application. Our results show that a load-proportional blinking policy combines the advantages of both activation and synchronous blinking for realistic Zipf-like popularity distributions and wind/solar power signals by achieving near optimal hit rates (within 15% of an activation policy), while also providing fairer access to the cache (within 2% of a syn- chronous policy) for equally popular objects.

/pdf/blink-managing-server-clusters-on-intermittent-power-4ffsmwr1ee.pdf

Blink: managing server clusters on intermittent power

To sustain perpetual operation, systems that harvest environmental energy must carefully regulate their usage to satisfy their demand. Regulating energy usage is challenging if a system's demands are not elastic and its hardware components are not energy-proportional, since it cannot precisely scale its usage to match its supply. Instead, the system must choose when to satisfy its energy demands based on its current energy reserves and predictions of its future energy supply. In this paper, we explore the use of weather forecasts to improve a system's ability to satisfy demand by improving its predictions. We analyze weather forecast, observational, and energy harvesting data to formulate a model that translates a weather forecast to a wind or solar energy harvesting prediction, and quantify its accuracy. We evaluate our model for both energy sources in the context of two different energy harvesting sensor systems with inelastic demands: a sensor testbed that leases sensors to external users and a lexicographically fair sensor network that maintains steady node sensing rates. We show that using weather forecasts in both wind- and solar-powered sensor systems increases each system's ability to satisfy its demands compared with existing prediction strategies.

/pdf/cloudy-computing-leveraging-weather-forecasts-in-energy-56xx2fg0om.pdf

Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems

Distributed generation (DG) uses many small on-site energy sources deployed at individual buildings to generate electricity. DG has the potential to make generation more efficient by reducing transmission and distribution losses, carbon emissions, and demand peaks. However, since renewables are intermittent and uncontrollable, buildings must still rely, in part, on the electric grid for power. While DG deployments today use net metering to offset costs and balance local supply and demand, scaling net metering for intermittent renewables to many homes is difficult. In this paper, we explore a different approach that combines residential TOU pricing models with on-site renewables and modest energy storage to incentivize DG. We propose a system architecture and control algorithm to efficiently manage the renewable energy and storage to minimize grid power costs at individual buildings. We evaluate our control algorithm by simulation using a collection of real-world data sets. Initial results show that the algorithm decreases grid power costs by 2.7X while nearly eliminating grid demand peaks, demonstrating the promise of our approach.

/pdf/the-case-for-efficient-renewable-energy-management-in-smart-hcnitmtlru.pdf

The case for efficient renewable energy management in smart homes

This paper presents a weighted center of mass based trilateration approach for locating a wireless device based on the signal strength received from the access points at known locations This approach mainly consists of two phases: (1) The calculation of distance from RSSI values of various access points as received by the mobile device, (2) Determination of the most probable location of wireless device using coordinates of various known or fixed access points and calculated distances of the device from those access points Kalman filter is also used in both phases in order to remove the measurement noise component and to increase the accuracy of estimation The proposed algorithm provides a solution for location tracking of mobile devices in indoor environment where the configuration of access points like transmit power etc, is not fixed and the movements in environment affecting attenuation of signal is so unpredictable that any mathematical modeling of indoor RF signal propagation is infeasible

Navin Sharma

Papers

Predicting solar generation from weather forecasts using machine learning

Blink: managing server clusters on intermittent power

Cloudy Computing: Leveraging Weather Forecasts in Energy Harvesting Sensor Systems

The case for efficient renewable energy management in smart homes

A weighted center of mass based trilateration approach for locating wireless devices in indoor environment