Despite the growing ubiquity of sensor deployments and the advances in sensor data analysis technology, relatively little attention has been paid to the spatial non-stationarity of sensed data which is an intrinsic property of the geographically distributed data. In this paper we deal with non-stationarity of geographically distributed data for the task of regression. At this purpose, we extend the Geographically Weighted Regression (GWR) method which permits the exploration of the geographical differences in the linear effect of one or more predictor variables upon a response variable. The parameters of this linear regression model are locally determined for every point of the space by processing a sample of weighted neighboring observations. Although the use of locally linear regression has proved appealing in the area of sensor data analysis, it also poses some problems. The parameters of the surface are locally estimated for every space point, but the form of the GWR regression surface is globally defined over the whole sample space. Moreover, the GWR estimation is founded on the assumption that all predictor variables are equally relevant in the regression surface, without dealing with spatially localized phenomena of collinearity. Our proposal overcomes these limitations with a novel tree-based approach which is adapted to the aim of recovering the functional form of a regression model only at the local level. A stepwise approach is then employed to determine the local form of each regression model by selecting only the most promising predictors and providing a mechanism to estimate parameters of these predictors at every point of the local area. Experiments with several geographically distributed datasets confirm that the tree based construction of GWR models improves both the local estimation of parameters of GWR and the global estimation of parameters performed by classical model trees.

The task being addressed in this paper consists of trying to forecast the future value of a time series variable on a certain geographical location, based on historical data of this variable collected on both this and other locations. In general, this time series forecasting task can be performed by using machine learning models, which transform the original problem into a regression task. The target variable is the future value of the series, while the predictors are previous past values of the series up to a certain p-length time window. In this paper, we convey information on both the spatial and temporal historical data to the predictive models, with the goal of improving their forecasting ability. We build technical indicators, which are summaries of certain properties of the spatio-temporal data, grouped in the spatio-temporal clusters and use them to enhance the forecasting ability of regression models. A case study with air temperature data is presented.

A smart grid can be seen as a sensor network, with immense amounts of grid sensor data continuously transmitted from various sensors. Mining knowledge from these data in order to enrich the grid with knowledge-based services is a challenging task due to the massive scale and spatial coupling therein. In this paper, we present a novel knowledge-based fault diagnosis service which is designed to detect faults in the energy production of a grid of PhotoVoltaic (PV) plants. A case study with a grid of PV plants distributed over the South of Italy is illustrated.

The Geographically Weighted Regression (GWR) is a method of spatial statistical analysis which allows the exploration of geographical differences in the linear effect of one or more predictor variables upon a response variable. The parameters of this linear regression model are locally determined for every point of the space by processing a sample of distance decay weighted neighboring observations. While this use of locally linear regression has proved appealing in the area of spatial econometrics, it also presents some limitations. First, the form of the GWR regression surface is globally defined over the whole sample space, although the parameters of the surface are locally estimated for every space point. Second, the GWR estimation is founded on the assumption that all predictor variables are equally relevant in the regression surface, without dealing with spatially localized collinearity problems. Third, time dependence among observations taken at consecutive time points is not considered as information-bearing for future predictions. In this paper, a tree-structured approach is adapted to recover the functional form of a GWR model only at the local level. A stepwise approach is employed to determine the local form of each GWR model by selecting only the most promising predictors. Parameters of these predictors are estimated at every point of the local area. Finally, a time-space transfer technique is tailored to capitalize on the time dimension of GWR trees learned in the past and to adapt them towards the present. Experiments confirm that the tree-based construction of GWR models improves both the local estimation of parameters of GWR and the global estimation of parameters performed by classical model trees. Furthermore, the effectiveness of the time-space transfer technique is investigated.

The growing integration of wind turbines into the power grid can only be balanced with precise forecasts of upcoming energy productions. This information plays as basis for operation and management strategies for a reliable and economical integration into the power grid. A precise forecast needs to overcome problems of variable energy production caused by fluctuating weather conditions. In this paper, we define a data mining approach, in order to process a past set of the wind power measurements of a wind turbine and extract a robust prediction model. We resort to a time series clustering algorithm, in order to extract a compact, informative representation of the time series of wind power measurements in the past set. We use cluster prototypes for predicting upcoming wind powers of the turbine. We illustrate a case study with real data collected from a wind turbine installed in the Apulia region.

With the development of AIS (Automatic Identification System), more and more vessels are equipped with AIS technology. Vessels' reports (e.g. position in geodetic coordinates, speed, course), periodically transmitted by AIS, have become an abundant and inexpensive source of ubiquitous motion information for the maritime surveillance. In this study, we investigate the problem of processing the ubiquitous data, which are enclosed in the AIS messages of a vessel, in order to display an interpolation of the itinerary of the vessel. We define a graph-aware itinerary mining strategy, which uses spatio-temporal knowledge enclosed in each AIS message to constrain the itinerary search. Experiments investigate the impact of the proposed spatio-temporal data mining algorithm on the accuracy and efficiency of the itinerary interpolation process, also when reducing the amount of AIS messages processed per vessel.

The growing integration of wind turbines into the power grid can only be balanced with precise forecasts of upcoming energy productions. This information plays as basis for operation and management strategies for a reliable and economical integration into the power grid. A precise forecast needs to overcome problems of variable energy production caused by fluctuating weather conditions. In this paper, we define a data mining approach, in order to process a past set of the wind power measurements of a wind turbine and extract a robust prediction model. We resort to a time series clustering algorithm, in order to extract a compact, informative representation of the time series of wind power measurements in the past set. We use cluster prototypes for predicting upcoming wind powers of the turbine. We illustrate a case study with real data collected from a wind turbine installed in the Apulia region.