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Like almost all measurement datasets, wind energy siting data are subject to data gaps that can for instance originate from a failure of the measurement devices or data loggers. This is in particular true for offshore wind energy sites where the harsh climate can restrict the accessibility of the measurement platform, which can also lead to much longer gaps than onshore. In this study, we investigate the impact of data gaps, in terms of a bias in the estimation of siting parameters and its mitigation by correlation and filling with mesoscale model data. Investigations are performed for three offshore sites in Europe, considering 2 years of parallel measurement data at the sites, and based on typical wind energy siting statistics. We find a mitigation of the data gaps' impact, i.e. a reduction of the observed biases, by a factor of 10 on mean wind speed, direction and Weibull scale parameter and a factor of 3 on Weibull shape parameter. With increasing gap length, the gaps' impact increases linearly for the overall measurement period while this behaviour is more complex when investigated in terms of seasons. This considerable reduction of the impact of the gaps found for the statistics of the measurement time series almost vanishes when considering long-term corrected data, for which we refer to 30 years of reanalysis data. [ABSTRACT FROM AUTHOR]

The objective of this paper was the experimental investigation of the accumulated induction effect of a large offshore wind farm as a whole, i.e. the global-blockage effect, in relation to atmospheric-stability estimates and wind farm operational states. We measured the inflow of a 400MW offshore wind farm in the German North Sea with a scanning long-range Doppler wind lidar. A methodology to reduce the statistical variability of different lidar scans at comparable measurement conditions was introduced, and an extensive uncertainty assessment of the averaged wind fields was performed to be able to identify the global-blockage effect, which is small compared to e.g. wind turbine wake effects and ambient variations in the inflow. Our results showed a 4% decrease in wind speed (accuracy range of 2% to 6 %) at transition piece height (24.6 m) upwind of the wind farm with the turbines operating at high thrust coefficients above 0.8 in a stably stratified atmosphere, which we interpreted as global blockage. In contrast, at unstable stratification and similar operating conditions and for situations with low thrust coefficients (i.e. approx. 0 for not operating turbines and ≤0.3 for turbines operating far above rated wind speed) we identified no wind speed deficit. We discussed the significance of our measurements and possible sources of error in long-range scanning lidar campaigns and give recommendations on how to measure small flow effects like global blockage with scanning Doppler lidar. In conclusion, we provide strong evidence for the existence of global blockage in large offshore wind farms in stable stratification and the turbines operating at a high thrust coefficient by planar lidar wind field measurements. We further conclude that global blockage is dependent on atmospheric stratification. [ABSTRACT FROM AUTHOR]

Control areas around the world use a variety of methods to ensure that resources added to meet future electricity loads create an adequate supply. This work describes a particular method for determining adequacy based on economic costs and benefits that applies equally well to dispatchable and nondispatchable resources. Analysis of the Northwest power supply indicates that energy efficiency and demand response may be the most cost-effective capacity resources for achieving an economic adequacy standard. [ABSTRACT FROM AUTHOR]

When facing a resource need, utilities should use all-source bidding to help identify the highest value resources to procure. By soliciting proposals from suppliers across all resource options, the utility applies to procurement the same important principles that guide integrated resource planning. All-source bidding is not new, but many utilities have not used it. This article explains how all-source bidding works, describes the benefits, and offers practical recommendations for utilities and utility regulators. [ABSTRACT FROM AUTHOR]

The current upward trend in large-scale integration of distributed energy resources (DER) in distribution networks has fueled interest in knowing their power quality issues (PQ), even in nearly real-time scale when possible. This trend involves researching new protection strategies that contribute to the reduction of supply interruptions times, which will ultimately result in greater energy efficiency. Based on this scenario, a research and development platform for testing the behavior of the electronic systems involved in supervision and control is presented. The proposed design is based on the experience acquired by our research group in the last ten years. The main objective is to reproduce on a laboratory scale the behavior of a 9 kVA power generation system associated with active loads. This setup allows reproducing all kind of perturbation that might happen in the actual grid and to verify the behavior of the system devoted to their detection. Therefore, the main interest of this system is to test the behavior of the physical devices, as well as the advanced algorithms for real-time detection. As one of its main characteristics, the setup makes use of a high-level synchronization system in order to guarantee the precise estimation of the response times. The most advanced objective is related to the global estimation of the response time considering the communication delays. Thus, the test platform contributes to the PQ, the protection, and the advanced communications, aspects which stand out as highly relevant in the imminent wide expansion of the Smart Grids. A case study, as well as the experimental results, are presented, which show and address the performance of the entire system. [ABSTRACT FROM AUTHOR]

Power as one of the most important energy to promote the development of national economy, plays an essential role in the normal operation of all aspects of society. Because of its production and use are difficult to store in large quantities, it is necessary to forecast future demand, which will become an important basis for making power development plans. Considering the complex non-linear relationship between power demand and its influencing factors, it is difficult to describe it accurately with the traditional mathematical models. In this paper, we select six major influencing factors and use the support vector machine to predict future power demand. The prediction accuracy is improved by parameter optimizing. At the same time, the simulation experiment of Shandong Province is conducted to further verify the validity and feasibility of the model. [ABSTRACT FROM AUTHOR]

In order to reduce the effect of numerical weather prediction (NWP) error on short term load forecasting (STLF) and improve the forecasting accuracy, a new hybrid model based on support vector regression (SVR) optimized by an artificial bee colony (ABC) algorithm (ABC-SVR) and seasonal autoregressive integrated moving average (SARIMA) model is proposed. According to the different day types and effect of the NWP error on forecasting prediction, working days and weekends load forecasting models are selected and constructed, respectively. The ABC-SVR method is used to forecast weekends load with large fluctuation, in which the best parameters of SVR are determined by the ABC algorithm. The working days load forecasting model is constructed based on SARIMA modified by ABC-SVR (AS-SARIMA). In the AS-SARIMA model, the ability of SARIMA to respond to exogenous variables is improved and the effect of NWP error on prediction accuracy is reduced more than with ABC-SVR. Contrast experiments are constructed based on International Organization for Standardization (ISO) New England load data. The experimental results show that prediction accuracy of the proposed method is less affected by NWP error and has higher forecasting accuracy than contrasting approaches. [ABSTRACT FROM AUTHOR]

The paper contributes to the limited literature modelling the term structure of crude oil markets. We explain the term structure of crude oil prices using the dynamic Nelson–Siegel model and propose to forecast oil prices using a generalized regression framework based on neural networks. The newly proposed framework is empirically tested on 24 years of crude oil futures prices covering several important recessions and crisis periods. We find 1-month-, 3-month-, 6-month- and 12-month-ahead forecasts obtained from a focused time-delay neural network to be significantly more accurate than forecasts from other benchmark models. The proposed forecasting strategy produces the lowest errors across all times to maturity. [ABSTRACT FROM AUTHOR]

Optimal automatic operation of buildings and their subsystems in responding to signals from a smart grid is essential to reduce energy demand, and to improve the power resilience. In order to achieve such automatic operation, high fidelity and computationally efficiency whole building energy forecasting models are needed. Currently, data-driven (black box) models and hybrid (grey box) models are commonly used in model based building control. However, typical black box models often require long training period and are bounded to building operation conditions during the training period. On the other hand, creating a grey box model often requires (a) long calculation time due to parameter optimization process; and (b) expert knowledge during the model development process. This paper attempts to quantitatively evaluate the impacts of two significant system characteristics: system nonlinearity and response time, on the accuracy of the model developed by a system identification process. A general methodology for building energy forecasting model development is then developed. How to adapt the system identification process based on these two characteristics is also studied. A set of comparison criteria are then proposed to evaluate the energy forecasting models generated from the adapted system identification process against other methods reported in the literature, including Resistance and Capacitance method, Support Vector Regression method, Artificial Neural Networks method, and N4SID subspace algorithm. Two commercial buildings: a small and a medium commercial building, with varying chiller nonlinearity, are simulated using EnergyPlus in lieu of real buildings for model development and evaluation. The results from this study show that the adapted system identification process is capable of significantly improve the performance of the energy forecasting model, which is more accurate and more extendable under both of the noise-free and noisy conditions than those models generated by other methods. [ABSTRACT FROM AUTHOR]

The building sector in Europe and Sweden accounts for a large part of the total European electricity end use. A large fraction of Swedish buildings are equipped with heat pumps for heating and a combination of heat pumps and decentralized energy generation from photovoltaic systems is an interesting system solution for reducing the energy use. It is important that the building has a high self-consumption of the generated PV-electricity. Self-consumption can be seen as energy conservation and has a considerable higher economic value than exported electricity for the building owner. A ground source heat pump with a novel weather forecast controller is simulated in Trnsys and compared to a reference case in regards to self-consumption and profitability. The economic analysis is based on the annuity method and a sensitivity analysis regarding annual cost, discount rate and annual electricity price change has been performed. The results indicates that the increase in self-consumed photovoltaic electricity is limited to 7% with the proposed novel weather forecast controller, which means that the controller is unprofitable. Because of this the proposed novel forecast controller is not a viable way of increasing self-consumption in systems with photovoltaic systems and ground source heat pumps in Sweden. [ABSTRACT FROM AUTHOR]

The structure of the Brazilian energy matrix defines Brazil as a global leader in power generation from renewable sources. In 2011, the share of renewable sources in electricity production reached 88.8%, mainly due to the large national water potential. Although the Brazilian energy model presents a strong potential for expansion, the total energy that could be used with most current renewable technologies often outweighs the national demand. The current composition of the national energy matrix has outstanding participation of hydropower, even though the country has great potential for the exploitation of other renewable energy sources such as wind, solar and biomass. This document therefore refers to the trend of evolution of the Brazilian Energy Matrix and exposes possible mitigation scenarios, also considering climate change. The methodology to be used in the modeling includes the implementation of the LEAP System (Long-range Energy Alternatives Planning) program, developed by the Stockholm Environment Institute, which allows us to propose different scenarios under the definition of socioeconomic scenarios and base power developed in the context of the REGSA project (Promoting Renewable Electricity Generation in South America). Results envision future scenarios and trends in power generation in Brazil, and the projected demand and supply of electricity for up to 2030. [ABSTRACT FROM AUTHOR]

The paper presents a methodology for energy demand forecasting which manifests at the Romanian society level. The accurate knowledge of the energy demand of a society allows it to plan its energy provision, formulation of strategies and recommendation of energetic policies. The proposed methodology is a heuristic one, which takes into account the evolution of the population, which is correlated to the possible evolution of the GDP/capita and to the evolution of the energetic intensity of the economy. [ABSTRACT FROM AUTHOR]

Energy consumption forecasting is a critical and necessary input to planning and controlling energy usage in the building sector which accounts for 40% of the world’s energy use and the world’s greatest fraction of greenhouse gas emissions. However, due to the diversity and complexity of buildings as well as the random nature of weather conditions, energy consumption and loads are stochastic and difficult to predict. This paper presents a new methodology for energy demand forecasting that addresses the heterogeneity challenges in energy modeling of buildings. The new method is based on a physical–statistical approach designed to account for building heterogeneity to improve forecast accuracy. The physical model provides a theoretical input to characterize the underlying physical mechanism of energy flows. Then stochastic parameters are introduced into the physical model and the statistical time series model is formulated to reflect model uncertainties and individual heterogeneity in buildings. A new method of model generalization based on a convex hull technique is further derived to parameterize the individual-level model parameters for consistent model coefficients while maintaining satisfactory modeling accuracy for heterogeneous buildings. The proposed method and its validation are presented in detail for four different sports buildings with field measurements. The results show that the proposed methodology and model can provide a considerable improvement in forecasting accuracy. [ABSTRACT FROM AUTHOR]

Distributed lag models (DLMs) to predict future internal temperatures have been developed using the hourly weather data and the internal temperatures recorded in eleven spaces on two UK National Health Service (NHS) hospital sites. The ward spaces were in five buildings of very different type and age. In all the DLMs, the best prediction of internal temperature was obtained using three exogenous drivers, previous internal temperature, external temperature and solar radiation. DLMs were sensitive to the buildings’ differences in orientation, thermal mass and shading and were validated by comparing the predictions with the internal temperatures recorded in the summer of 2012. The results were encouraging, with both modelled and recorded data showing good correlation. To understand the resilience of the spaces to heat waves, the DLMs were fed with weather data recorded during the hot summer of 2006. The Nightingale wards and traditional masonry wards showed remarkable resilience to the hot weather. In contrast, light-weight modular buildings were predicted to overheat dangerously. By recording internal temperatures for a short period, DLMs might be created that can forecast future temperatures in many other types of naturally ventilated or mixed-mode buildings as a means of assessing overheating risk. [ABSTRACT FROM AUTHOR]

Photovoltaic power forecasting (PVPF) can help energy management system and power grid to improve the proportion of solar energy in total energy consumption. Classification modeling according to different weather types is an effective means to improve the accuracy of PVPF under various weather statuses. However, the weather type of historical data (WTHD) is missing in some cases, which will cause great difficulties to classification modeling because the data without WTHD cannot be used for the model training. To identify the missing WTHD, a solar irradiance feature extraction and support vector machines (SVM) based weather statuses pattern recognition (WSPR) model for short-term PVPF (ST-PVPF) is presented. To ensure the feasibility and reduce the workload of classification modeling, four generalized weather classes (GWC) covering all weather types are constituted, and GWC based classification modeling approach for ST-PVPF is proposed subsequently. The SVM model for WSPR is built with input features extracted from solar irradiance data. Through a case study, the effectiveness and performance of the WSPR model are verified and evaluated. The influences of different input dimensions and feature combinations are also analyzed and discussed. The results indicate that the missing WTHD can be effectively recovered as GWC by the proposed model. [ABSTRACT FROM AUTHOR]

Accurate and reliable forecast of wind power is essential to power system operation and control. However, due to the nonstationarity of wind power series, traditional point forecasting can hardly be accurate, leading to increased uncertainties and risks for system operation. This paper proposes an extreme learning machine (ELM)-based probabilistic forecasting method for wind power generation. To account for the uncertainties in the forecasting results, several bootstrap methods have been compared for modeling the regression uncertainty, based on which the pairs bootstrap method is identified with the best performance. Consequently, a new method for prediction intervals formulation based on the ELM and the pairs bootstrap is developed. Wind power forecasting has been conducted in different seasons using the proposed approach with the historical wind power time series as the inputs alone. The results demonstrate that the proposed method is effective for probabilistic forecasting of wind power generation with a high potential for practical applications in power systems. [ABSTRACT FROM PUBLISHER]

Accurate and reliable wind power forecasting is essential to power system operation. Given significant uncertainties involved in wind generation, probabilistic interval forecasting provides a unique solution to estimate and quantify the potential impacts and risks facing system operation with wind penetration beforehand. This paper proposes a novel hybrid intelligent algorithm approach to directly formulate optimal prediction intervals of wind power generation based on extreme learning machine and particle swarm optimization. Prediction intervals with associated confidence levels are generated through direct optimization of both the coverage probability and sharpness to ensure the quality. The proposed method does not involve the statistical inference or distribution assumption of forecasting errors needed in most existing methods. Case studies using real wind farm data from Australia have been conducted. Comparing with benchmarks applied, experimental results demonstrate the high efficiency and reliability of the developed approach. It is therefore convinced that the proposed method provides a new generalized framework for probabilistic wind power forecasting with high reliability and flexibility and has a high potential of practical applications in power systems. [ABSTRACT FROM PUBLISHER]

Highlights: [•] We investigate unit commitment deploying stochastic and deterministic approaches. [•] We consider day-ahead markets, its forecast and weekly price based unit commitment. [•] Stochastic and deterministic unit commitment are identical for the first planning day. [•] Unit commitment and bidding policy can be based on the deterministic approach. [•] Robust forecasting models should be estimated based on the whole planning horizon. [ABSTRACT FROM AUTHOR]

The paper illustrates an adaptive approach based on different topologies of artificial neural networks (ANNs) for the power energy output forecasting of photovoltaic (PV) modules. The analysis of the PV module's power output needed detailed local climate data, which was collected by a dedicated weather monitoring system. The Department of Energy, Information Engineering, and Mathematical Models of the University of Palermo (Italy) has built up a weather monitoring system that worked together with a data acquisition system. The power output forecast is obtained using three different types of ANNs: a one hidden layer Multilayer perceptron (MLP), a recursive neural network (RNN), and a gamma memory (GM) trained with the back propagation. In order to investigate the influence of climate variability on the electricity production, the ANNs were trained using weather data (air temperature, solar irradiance, and wind speed) along with historical power output data available for the two test modules. The model validation was performed by comparing model predictions with power output data that were not used for the network's training. The results obtained bear out the suitability of the adopted methodology for the short-term power output forecasting problem and identified the best topology. [ABSTRACT FROM AUTHOR]

Large scale integration of wind generation capacity into power systems introduces operational challenges due to wind power uncertainty and variability. Therefore, accurate wind power forecast is important for reliable and economic operation of the power systems. Complexities and nonlinearities exhibited by wind power time series necessitate use of elaborative and sophisticated approaches for wind power forecasting. In this paper, a local neurofuzzy (LNF) approach, trained by the polynomial model tree (POLYMOT) learning algorithm, is proposed for short-term wind power forecasting. The LNF approach is constructed based on the contribution of local polynomial models which can efficiently model wind power generation. Data from Sotavento wind farm in Spain was used to validate the proposed LNF approach. Comparison between performance of the proposed approach and several recently published approaches illustrates capability of the LNF model for accurate wind power forecasting. [ABSTRACT FROM AUTHOR]

This paper presents a novel hybrid intelligent algorithm based on the wavelet transform (WT) and fuzzy ARTMAP (FA) network for forecasting the power output of a wind farm utilizing meteorological information such as wind speed, wind direction, and temperature. The prediction capability of the proposed hybrid WT +FA model is demonstrated by an extensive comparison with a benchmark persistence method, other soft computing models, and hybrid models as well. The test results show a significant improvement in forecasting error through the application of a proposed hybrid WT +FA model. The proposed hybrid wind power forecasting strategy is applied to real-life data from Kent Hill wind farm located in New Brunswick, Canada. [ABSTRACT FROM AUTHOR]

Considering the inherent variability and uncertainty of wind power generation, in this study, a self-organizing map (SOM) combined with rough set theory clustering technique (RST) is proposed to extract the relative knowledge and to choose the most similar history situation and efficient data for wind power forecasting with numerical weather prediction (NWP). Through integrating the SOM and RST methods to cluster the historical data into several classes, the approach could find the similar days and excavate the hidden rules. According to the data reprocessing, the selected samples will improve the forecast accuracy echo state network (ESN) trained by the class of the forecasting day that is adopted to forecast the wind power output accordingly. The developed methods are applied to a case of power forecasting in a wind farm located in northwest of China with wind power data from April 1, 2008, to May 6, 2009. In order to verify its effectiveness, the performance of the proposed method is compared with the traditional backpropagation neural network (BP). The results demonstrated that knowledge mining led to a promising improvement in the performance for wind farm power forecasting. [ABSTRACT FROM AUTHOR]