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Forecasting of wind speed and wind power generation is indispensable for the effective operation of a wind farm and the optimal management of revenue and risks. Hybrid forecasting of time series data is considered to be a potentially effective alternative compared with the conventional single forecasting modeling approaches such as autoregressive integrated moving average (ARIMA) and artificial neural...
Nowadays, advanced metering infrastructure allows us to record building energy consumption at much higher resolutions. This paper presents the methods for building load profile analysis and forecast using the 15-minute data collected at a substation feeder at the Centennial Campus of North Carolina State University from May 2012 to April 2014. Building load signatures for benchmarking building load...
To improve forecasting accuracy for baseline load and load impact from demand response resources, this paper develops three innovative statistical models. These models are regression spline fixed effect model, fixed effect change point model and mixed effect change point model. The models developed are applied to forecast baseline load and load impact from air conditioning cycling demand response...
One of the most effective and cheapest strategies to integrate the fast-increasing number of solar-based generators is by forecasting their power output to schedule power dispatch, energy storages, manage backup generators, and compensate for any fluctuations of solar power. In particular, forecasting the insolation is invaluable to predict the power generated by photovoltaic arrays. An essential...
Among renewable power sources, wind energy is the most promising technology; however, the inter-temporal uncertainty of this source makes impossible its massive integration. Forecasting of wind generation is a key factor for the economical operation of the power system. Thus, the error related to this process is typically modeled by means of a determined probability distribution to be later incorporated...
Energy Storage Systems (ESS) can offer a combination of services to transmission and distribution network operators and energy suppliers. In order to do this effectively, the power and energy resources of the energy storage system must be allocated ahead of time, and account for uncertainty in service delivery. This paper presents a method for scheduling these resources, which accounts for the limited...
This paper proposes a novel TRansformation Under STability-reTraining Equilibrium CHaracterization (Trust-Tech) enhanced methodology for the solar energy prediction. This novel method is an ensemble of Trust-Tech-enhanced, group-based genetic algorithm (GA)-assisted SVM predictors. Several distinguished features of the proposed method are as follows: Firstly, feature selection algorithm is used to...
The hybrid forecasting algorithm, based on empirical mode decomposition (EMD), has attracted considerable attentions and been widely applied to forecast electricity load, wind speed, and solar irradiation time series (TS). The basic idea of the EMD based method is to decompose the complicated original TS into a collection of sub-series and build specific forecasting models for individual sub-series...
Machine learning methods are main stream algorithms applied in short term load forecasting. However, typical machine learning methods consisting of Artificial Neural Network (ANN) and Support Vector Regression (SVR) have deficiencies hard to overcome, such as easy to be trapped in local optimization (for ANN) or hard to decide kernel parameter and penalty parameter (for SVR). On the other hand, grey...
A new uncertainty quantification (UQ) algorithm for the error analysis of solar power forecasting is introduced. In solar power forecasting, there is a strong need for lenders, operators, traders, and Virtual Power Plant (VPP) to evaluate the forecasting results provided by different forecast providers. The algorithm potentially evaluates the third party's forecast to increase the performance of VPP...
Improving the precision of wind power forecasting can be helpful to dispatch efficiency. In this paper, to examine the time varying characteristics in the high order moments of wind power time series, an improved auto-regressive conditional density (ARCD) model for wind power forecasting is proposed. First, a generalized form of ARCD model is presented. Furthermore, from three different aspects: skewed...
This paper introduces an application of the Gaussian Conditional Random Fields (GCRF) model for forecasting the solar power in electricity grids. The introduced forecasting technique is capable of modeling both the spatial and temporal correlations of various solar generation stations. It will be demonstrated in this paper how the suggested solution can significantly improve the forecast accuracy...
This paper presents the forecasting algorithms for determining the electricity usage and operation status of residential heating, ventilating, and air conditioning (HVAC) systems. Two algorithms are presented based on what types of measured data can be received by the home energy management system (HEMS). Algorithm 1 is developed assuming only HVAC status is available to forecast the future HVAC usage...
This paper proposes an approach for distribution system state forecasting, which aims to provide an accurate and high speed state forecasting with an optimal synchrophasor sensor placement (OSSP) based state estimator and an extreme learning machine (ELM) based forecaster.
Due to its economical and environmental benefits to society and industry, integrating solar power is continuously growing in many utilities and Independent System Operators (ISOs). However, the intermittent nature of the renewable energy brings new challenges to the system operators. One key to resolve this problem is to have a ubiquitously efficient solar power output forecasting system, so as to...
This paper proposes a new objective function and quantile regression (QR) algorithm for load forecasting (LF). In LF, the positive forecasting errors often have different economic impact from the negative forecasting errors. Considering this difference, a new objective function is proposed to put different prices on the positive and negative forecasting errors. QR is used to find the optimal solution...
Load forecasting is an important task in power system operations. Considering the strong correlation between electricity load demand and weather condition, the temperature has always been an input for short-term load forecasting. For day-ahead load forecasting, the whole next-day's temperature forecast (say, hourly or half-hourly forecast) is however sometimes difficult to obtain or suffering from...
Increased penetration of renewable energy based generators throughout modern distribution networks makes it crucial to seek elevated levels of accuracy in forecasting methods. This paper presents a new load forecasting method for residential distribution feeders. It uses, load time series decomposition to distinguish between all types of loads and events on feeder. Then, a generalized regression neural...
Due to the rapid growth of wind power generation in the recent years, accurate wind power prediction is necessary for reliable power system operation. This paper proposes a novel forecasting algorithm for day-ahead wind power forecasting. In the presented model, instead of relying on the gradient-descent approach, a meta-heuristic optimization method called shuffled frog leaping algorithm (SFLA) is...
Managing a reliable, renewable, and affordable power grid is a challenging task because the mix of power generating and consuming devices connected to the network continues to change. Improved forecasts help network operators respond to these changes and make data-driven decisions regarding, e.g., demand response and market operations.
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