Internet of Things (IoT) is a connection of people and things at any time, in any place, with anyone and anything, using any network and any service. Thus, IoT is a huge dynamic global network ...infrastructure of Internet-enabled entities with web services. One of the most important applications of IoT is the Smart Grid (SG). SG is a data communications network which is integrated with the power grid to collect and analyze data that are acquired from transmission lines, distribution substations, and consumers. In this paper, we talk about IoT and SG and their relationship. Some IoT architectures in SG, requirements for using IoT in SG, IoT applications and services in SG, and challenges and future work are discussed.
A randomized control field experiment is reported using in-home displays to reduce household electricity consumption. Custom-coded in-home displays (IHDs) were created to provide real-time household ...electricity consumption feedback, and were framed as either (a) simple kW consumption, or (b) kW consumption and the corresponding cost, or (c) kW consumption and a dynamically-derived social normative frame. Analyses focused on household electricity consumption in the first week following deployment, and again over a 3-month time span. Findings showed that households receiving simple feedback, and feedback framed as cost did not differ significantly from the randomized control at either the 1-week or the 3-month time points. Similarly, results showed that educational materials alone did not reduce electricity consumption. However, significant effects were found for households receiving the normative frame, which consumed 9% less than control households during the initial 1-week evaluation period, and 7% less during the full 3-month evaluation period. Yet despite these findings, residents reported more positive experiences and more obligations to conserve electricity with the cost and feedback IHDs. The results suggest that in-home displays offer promise for encouraging energy conservation, but careful consideration should be given to the way that the feedback is framed.
•Providing residents with normative framed electricity feedback reduced energy use.•Cost-framed feedback is preferred by residents, but did not reduce energy use.•Information increases knowledge about energy use, but does not reduce consumption.•Special consideration should be given to the frame accompanying real-time feedback.
With the development of automation in distribution systems, distribution supervisory control and data acquisition (SCADA) and many automated meter reading (AMR) systems have been installed on ...distribution systems. Also distribution management system (DMS) have advanced and include more sophisticated analysis tools. The combination of these developments is providing a platform for development of distribution system state estimation (DSE). A branch-current-based three-phase state estimation algorithm for distribution systems has been developed and tested. This method chooses the magnitude and phase angle of the branch current as the state variables. Because of the limited number of real-time measurements in the distribution system, the state estimator can not acquire enough real-time measurements for convergence, so pseudo-measurements are necessary for distribution system state estimator. The load estimated at every node from the AMR systems is used as a pseudo-measurement for the state estimator. The algorithm has been tested on three IEEE radial test feeders. In addition to this new strategy for DSE, another issue is meter-placement. This topic includes the type of measurement as well as the location of the measurement. Our results show the impact of these two issues on accuracy. Several general meter rules based on this analysis are outlined.
•Propose a novel pinball loss guided long short-term memory network.•Design probabilistic forecasting model for individual consumers.•Conduct comprehensive comparisons with the-state-of-the-art ...methods.•Conduct case studies on open dataset and large number of consumers.
The installation of smart meters enables the collection of massive fine-grained electricity consumption data and makes individual consumer level load forecasting possible. Compared to aggregated loads, load forecasting for individual consumers is prone to non-stationary and stochastic features. In this paper, a probabilistic load forecasting method for individual consumers is proposed to handle the variability and uncertainty of future load profiles. Specifically, a deep neural network, long short-term memory (LSTM), is used to model both the long-term and short-term dependencies within the load profiles. Pinball loss, instead of the mean square error (MSE), is used to guide the training of the parameters. In this way, traditional LSTM-based point forecasting is extended to probabilistic forecasting in the form of quantiles. Numerical experiments are conducted on an open dataset from Ireland. Forecasting for both residential and commercial consumers is tested. Results show that the proposed method has superior performance over traditional methods.
The installation of smart meters enabling electricity load to be measured with half-hourly granularity provides an innovative demand-side management opportunity that is likely to be advantageous for ...both utility companies and customers. Time-of-use tariffs are widely considered to be the most promising solution for optimising energy consumption in the residential sector. Although there exists a large body of research on demand response in electricity pricing, a practical framework to forecast user adaptation under different Time-of-use tariffs has not been fully developed. The novelty of this work is to provide the first top-down statistical modelling of residential customer demand response following the adoption of a Time-of-use tariff and report the model's accuracy and the feature importance. The importance of statistical moments to capture various lifestyle constraints based on smart meter data, which enables this model to be agnostic about household characteristics, is discussed. 646 households in Ireland during pre/post-intervention of Time-of-use tariff is used for validation. The value of Mean Absolute Percentage Error in forecasting average load for a group of households with the Random Forest method investigated is 2.05% for the weekday and 1.48% for the weekday peak time.
•A model to forecast user adaptation under different Time-of-use tariffs.•Lifestyle constraints are considered as key inputs in the form of statistical moments.•The model requires only a half-hourly sampled historical smart meter data.•Random Forest outperforms Neural Network and Linear Regression models.•MAPE of the best model reports 2.05% for the weekday.
The manuscript describes a simplified methodology with which to assess the economic level of apparent losses (ELAL) in a water utility. This economic point corresponds to the break-even point for ...which the marginal benefit of increasing the frequency of the apparent losses’ reduction activities equalizes the marginal cost of their implementation. For this calculation, each apparent loss component, as defined by the International Water Association, has been subdivided into two additional categories. These categories have been established depending on how periodic activities conducted by the water utility to reduce apparent losses—namely water meter replacement and customers’ connection inspections—may affect their magnitude. It has been found that the ELAL is influenced by intervention costs, the degradation rate of the accuracy of water meters and water tariffs. In addition, this work defines a set of performance indicators to benchmark the apparent loss’s performance relative to the minimum achievable and optimum levels of the losses. Finally, two case studies on how the proposed calculation should be applied have been added to the appendices.
This paper develops a computationally fast algorithm to disaggregate the total energy use recorded by smart heat meters into space heating (SH) and domestic hot water (DHW). The algorithm trains a ...regression model on SH-only hours and predicts SH for all other hours with potential DHW use. Data from smart water meters were used to identify hours with only SH. Assessing 13 regression models, an untuned random forest was identified as the best-performing regression model with an acceptable computational cost (median: 7.4s per building). Furthermore, using one year of data from over 2400 single-family homes, it was shown that the best result (median CVRMSE: 0.13) can be obtained using only smart heat meters based regressors, increasing the applicability of the developed method. Furthermore, two tests were implemented, and it was successfully demonstrated that they identify situations where the regressors are distributed differently for hours with SH only and hours with SH and DHW, to identify possible training bias; thus, buildings where the disaggregation is potentially unreliable. Validation against data from three single-family houses with known SH and DHW use confirmed the good performance of the method and that the performance can be estimated without ground truth data via nested cross-validation.
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•Calculation of space heating (SH) and domestic hot water (DHW) from total energy use.•Requires only hourly data from smart heat and water meters; is computationally fast.•Developed using one year’s data from over 2400 single-family houses.•Method validated against ground truth data from 3 single family houses.•Accuracy estimate can be obtained without labelled known data.
Load super-resolution reconstruction (LSR) has become an active direction in recent years. This letter proposes a new LSR technique. By training the LSR model only one time, the system is capable of ...reconstructing a low-resolution load profile to be load profiles with different high resolutions that could be arbitrarily specified by the user of the system. Experiments based on a real-world dataset are conducted to validate the ability and effectiveness of the LSR system.
The smart metering infrastructure plays an important role in smart grid environments. Such metering networks need to be protected against cyber attacks by using authenticated key exchange protocols, ...and many relevant schemes have been presented by researchers. In addition, in order to protect against the energy theft problem, it is also important to consider physical security of the smart meter. Recently, PUFs (physical uncloneable functions) have gained popularity as a primitive against physical attacks. In 2019, we proposed the first PUF-based authentication scheme for secure smart grid communication with resilience against physical attacks on smart meters. However, recent studies have shown that PUFs are susceptible to modeling attacks. To address this issue, this paper proposes a reconfigurable authenticated key exchange scheme for secure communication in smart grids by using the concept of reconfigurable PUFs. In addition to security, the efficiency evaluation demonstrates that our new scheme has advantages in both the computation and communication costs as compared to the state-of-the-art protocols.
Analog dial are widely used in modern production and life. In the fields of meter detection and meter scale value recording, the demand for automatic reading of analog dial is increasing. This paper ...proposes an improved method of fast reading of images, which refines the scale circle and uses the branch point extraction algorithm to extract the intersection of the scale line and the scale circle to express the scale line indirectly. Finally, the pointer reading is calculated based on the ratio of the deflection angle of the pointer to the total angle of the range.
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