Short term load forecasting technique based on the seasonal exponential adjustment method and the regression model

For an energy-limited economy system, it is crucial to forecast load demand accurately. This paper devotes to 1-week-ahead daily load forecasting approach in which load demand series are predicted by employing the information of days before being similar to that of the forecast day. As well as in many nonlinear systems, seasonal item and trend item are coexisting in load demand datasets. In this paper, the existing of the seasonal item in the load demand data series is firstly verified according to the Kendall τ correlation testing method. Then in the belief of the separate forecasting to the seasonal item and the trend item would improve the forecasting accuracy, hybrid models by combining seasonal exponential adjustment method (SEAM) with the regression methods are proposed in this paper, where SEAM and the regression models are employed to seasonal and trend items forecasting respectively. Comparisons of the quartile values as well as the mean absolute percentage error values demonstrate this forecasting technique can significantly improve the accuracy though models applied to the trend item forecasting are eleven different ones. This superior performance of this separate forecasting technique is further confirmed by the paired-sample T tests. © 2013 Elsevier B.V. All rights reserved

Large-Scale Measurements and Prediction of DC-WAN Traffic

Large cloud service providers have built an increasing number of geo-distributed data centers (DCs) connected by Wide Area Networks (WANs). These DC-WANs carry both high-priority traffic from interactive services and low-priority traffic from bulk transfers. Given that a DC-WAN is an expensive resource, providers often manage it via traffic engineering algorithms that rely on accurate predictions of inter-DC high-priority (delay-sensitive) traffic. In this article, we perform a large-scale measurement study of high-priority inter-DC traffic from Baidu. We measure how inter-DC traffic varies across their global DC-WAN and show that most existing traffic prediction methods either cannot capture the complex traffic dynamics or overlook traffic interrelations among DCs. Building on our measurements, we propose the In terrelated- Te mporal G raph Convolutional Net work (IntegNet) model for inter-DC traffic prediction. In contrast to prior efforts, our model exploits both temporal traffic patterns and inferred co-dependencies between DC pairs. IntegNet forecasts the capacity needed for high-priority traffic demands by accounting for the balance between resource provisioning (i.e., allocating resources exceeding actual demand) and QoS losses (i.e., allocating fewer resources than actual demand). Our experiments show that IntegNet can keep a very limited QoS loss, while also reducing overprovisioning by up to 42.1% compared to the state-of-the-art and up to 66.2% compared to the traditional method used in DC-WAN traffic engineering

Emerging Technologies for the Energy Systems of the Future

Energy systems are transiting from conventional energy systems to modernized and smart energy systems. This Special Issue covers new advances in the emerging technologies for modern energy systems from both technical and management perspectives. In modern energy systems, an integrated and systematic view of different energy systems, from local energy systems and islands to national and multi-national energy hubs, is important. From the customer perspective, a modern energy system is required to have more intelligent appliances and smart customer services. In addition, customers require the provision of more useful information and control options. Another challenge for the energy systems of the future is the increased penetration of renewable energy sources. Hence, new operation and planning tools are required for hosting renewable energy sources as much as possible

New input identification and artificial intelligence based techniques for load prediction in commercial building

The accuracy of prediction models for electrical loads are important as the predicted result can affect processes related to energy management such as maintenance planning, decision-making processes, as well as cost and energy savings. The studies on improving load prediction accuracy using Least Squares Support Vector Machine (LSSVM) are widely carried out by optimizing the LSSVM hyper-parameter which includes the Kernel parameter and the regularization parameter. However, studies on the effects of input data determination for the LSSVM have not widely tested by researchers. This research developed an input selection technique using Modified Group Method of Data Handling (MGMDH) to improve the accuracy of buildings load forecasting. In addition, a new cascaded Group Method of Data Handing (GMDH) and LSSVM (GMDH-LSSVM) model is developed for electrical load prediction to improve the prediction accuracy of LSSVM model. To further improve the prediction model ability, a Modified GMDH has been cascaded to the LSSVM model to enhance the accuracy of building electrical load prediction and reduce the complexity of GMDH model. The proposed models are compared with GMDH model, LSSVM model and Artificial Neural Network (ANN) model to observe the prediction performance. The performances of prediction for each tested models are evaluated using the Mean Absolute Percentage Error (MAPE). In this analysis, the proposed prediction model, gives 33.82% improvement of prediction accuracy as compared to LSSVM model. From this research, it can be concluded that cascading the models can improve the prediction accuracy and the proposed models can be used to predict building electrical loads