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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]

Solar energy is expected to provide a major contribution to the future global energy supply, while helping the transition toward a carbon-free economy. Because of its variable character, its efficient use will necessitate trustworthy forecast information of its availability in a variety of spatial and time scales, depending on application. This study proposes a new forecasting approach for irradiance time series that combines mutual information measures and an Extreme Learning Machine (ELM). The method is referred to as Minimum Redundancy – Maximum Relevance (MRMR). To assess the proposed approach, its performance is evaluated against four scenarios: long window (latest 50 variables), short window (latest 5 variables), standard Principal Components Analysis (PCA) and clear-sky model. All these scenarios are applied to three typical forecasting horizons (15-min ahead, 1-h ahead and 24-h ahead). Based on measured irradiance data from 20 sites representing a variety of climates, the test results reveal that the selection of a good set of relevant variables positively impacts the forecasting performance of global solar radiation. The present findings show that the proposed approach is able to improve the accuracy of solar irradiance forecasting compared to other proposed scenarios. [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]

SUMMARY A method is proposed for forecasting global solar radiation. The method is based on weather information using binary trees and factor analysis. The feature of this method is that it is possible to use a simple linear forecasting equation. The method has been tested on meteorological and global solar radiation data obtained at several observation sites, and the results show that it is a promising means of maintaining the balance between demand and supply of electric power in power systems of the near future with a large number of photovoltaic systems installed. [ABSTRACT FROM AUTHOR]

This article addresses two issues in solar energy forecasting from the numerical weather prediction (NWP) models using machine learning. First, we are interested in determining the relevant information for the forecasting task. With this purpose, a study has been carried out to evaluate the influence on accuracy of the number of NWP grid nodes used as input for the forecasting model, as well as their relative importance. Several machine learning (support vector machines and gradient boosting) and feature selection algorithms (linear, ReliefF, and local information analysis) have been used in this study. The second aim is to be able to predict solar energy for locations where no previous production data are available. To address this goal, an approach consisting on modeling regions in the grid is proposed. Models (aggregate models) use as input attributes the meteorological variables relevant for the region and two new inputs to identify the location of each station: the latitude and the longitude. Those models can be used to predict energy production for existing stations and for new locations, represented by latitude and longitude. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Effective energy planning and governmental decision-making policies heavily rely on accurate forecast of energy demand. This paper discusses and compares five different forecasting techniques to model energy demand in the United States using economic and demographic factors. Two artificial neural network (ANN) models, two regression analysis models, and one autoregressive integrated moving average (ARIMA) model are developed based on the historical data from 1950 to 2013. While ANN model I and regression model I use gross domestic product (GDP), gross national product (GNP), and per capita personal income as independent input factors, ANN model 2 and regression model 2 employ GDP, GNP, and population (POP) as the predictive factors. The forecasted values resulted from these models are compared with the forecast made by the US. Energy Information Administration (EIA) for the period of 2014-2019. The forecasted results of ANN models and regression model 1 are dose to those of the US. El A; however, the results of regression model 2 and ARIMA model are significantly different from the forecast made by the US. El A. Finally, a comparison of the forecasted values resulted from three efficient models showed that the energy demand would vary between 95.51 and 100.08 quadrillion British thermal unit (btu) for the period of 2014-2019. In addition, we have discussed the possibility of self-sufficiency of the United States in terms of energy generation based on the information of current available technologies nationwide. [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 integration of wind power in electricity generation brings new challenges to the unit commitment problem, as a result of the random nature of the wind speed. The scheduling of thermal generation units at the day-ahead stage is usually based on wind power forecasts. Due to technical limitations of thermal units, deviations from those forecasts during intra-day operations may lead to unwanted consequences, such as load shedding and increased operating costs. Wind power forecasting uncertainty has been handled in practice by means of conservative stochastic scenario-based optimization models, or through additional operating reserve settings. However, generation companies may have different attitudes towards the risks associated to wind power variability. In this paper, operating costs and load shedding are modeled by non-linear utility functions aggregated into a single additive utility function of a multi-objective model. Computational experiments have been done to validate the approach: firstly we test our model for the wind–thermal unit commitment problem and, in a second stage, pumped storage hydro units are added, leading to a model with wind–hydro-thermal coordination. Results have shown that the proposed methodology is able to correctly reflect different risk profiles of decision makers for both models. [ABSTRACT FROM AUTHOR]

To obtain operation benefits of both distribution network (DN) and microgrids (MGs), a multi-objective bi-level optimal operation model for DN with grid-connected MGs is explained. Starting from forecast of load and generation in MGs, upper-level (DN level) model determines the optimal dispatch of DN to achieve its power loss reduction and voltage profile improvement. Lower-level (MG level) model accepts the dispatch requirements from upper-level and determines the optimal operation strategy of distributed generators in MGs. Their energy utilization is increased with the consideration of wind curtailment, solar curtailment and other factors such as environmental benefits. With the mutual influence and constraints between upper-level and lower-level model, MGs could accommodate the optimal dispatch of DN. To solve the bi-level model, a combination method based on self-adaptive genetic algorithm and non-linear programming is put forward. IEEE 33 DN with Europe typical MGs and a real system are presented to perform several simulations, and results show the over-all optimal operation schemes of both DN and MGs compared with traditional dispatch approach, thereby validating the efficiency of the proposed model. [ABSTRACT FROM AUTHOR]

Larger amounts of variable renewable energy sources bring about larger amounts of uncertainty in the form of forecast errors. When taking operational and planning decisions under uncertainty, a trade-off between risk and costs must be made. Today's deterministic operational tools, such as N-1-based methods, cannot directly account for the underlying risk due to uncertainties. Instead, several definitions of operating risks, which are probabilistic indicators, have been proposed in the literature. Estimating these risks require estimating very low probabilities of violations of operating constraints. Crude Monte-Carlo simulations are very computationally demanding for estimating very low probabilities. In this paper, an importance sampling technique from mathematical finance is adapted to estimate very low operating risks in power systems given probabilistic forecasts for the wind power and the load. Case studies in the IEEE 39 and 118 bus systems show a decrease in computational demand of two to three orders of magnitude. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid methodology based on Support Vector Regression for wind speed forecasting is proposed. Using the autoregressive model called Time Delay Coordinates, feature selection is performed by the Phase Space Reconstruction procedure. Then, a Support Vector Regression model is trained using univariate wind speed time series. Parameters of Support Vector Regression are tuned by a genetic algorithm. The proposed method is compared against the persistence model, and autoregressive models (AR, ARMA, and ARIMA) tuned by Akaike's Information Criterion and Ordinary Least Squares method. The stationary transformation of time series is also evaluated for the proposed method. Using historical wind speed data from the Mexican Wind Energy Technology Center (CERTE) located at La Ventosa, Oaxaca, México, the accuracy of the proposed forecasting method is evaluated for a whole range of short termforecasting horizons (from 1 to 24 h ahead). Results show that, forecasts made with our method are more accurate for medium (5–23 h ahead) short term WSF and WPF than those made with persistence and autoregressive models. [ABSTRACT FROM AUTHOR]

In order to perform predictions of a photovoltaic (PV) system power production, a neural network architecture system using the Nonlinear Autoregressive with eXogenous inputs (NARX) model is implemented using not only local meteorological data but also measurements of neighbouring PV systems as inputs. Input configurations are compared to assess the effects of the different inputs. The added value of the information of the neighbouring PV systems has demonstrated to further improve the accuracy of predictions for both winter and summer seasons. Additionally, forecasts up to 1 month are tested and compared with a persistence model. Normalized root mean square errors (nRMSE) ranged between 9% and 25%, with the NARX model clearly outperforming the persistence model for forecast horizons greater than 15 min. [ABSTRACT FROM AUTHOR]

This study focus on the minimum duration of training data required for PV generation forecast. In order to investigate this issue, the study is implemented on 2 PV installations: the first one in Guadeloupe represented for tropical climate, the second in Lille represented for temperate climate; using 3 different forecast models: the Scaled Persistence Model, the Artificial Neural Network and the Multivariate Polynomial Model. The usual statistical forecasting error indicators: NMBE, NMAE and NRMSE are computed in order to compare the accuracy of forecasts. The results show that with the temperate climate such as Lille, a longer training duration is needed. However, once the model is trained, the performance is better. [ABSTRACT FROM AUTHOR]

As a type of clean and renewable energy source, wind power is widely used. However, owing to the uncertainty of wind speed, it is essential to build an accurate forecasting model for large-scale wind power penetration. Numerical weather prediction (NWP) and data-driven modeling are two typical paradigms. NWP is usually unavailable or spatially insufficient. Data-driven modeling is an effective candidate. As to some newly-built wind farms, sufficient historical data is not available for training an accurate model, while some older wind farms may have long-term wind speed records. A question arises regarding whether the prediction model trained by data coming from older farms is also effective for a newly-built farm. In this paper, we propose an interesting trial of transferring the information obtained from data-rich farms to a newly-built farm. It is well known that deep learning can extract a high-level representation of raw data. We introduce deep neural networks, trained by data from data-rich farms, to extract wind speed patterns, and then finely tune the mapping with data coming from newly-built farms. In this way, the trained network transfers information from one farm to another. The experimental results show that prediction errors are significantly reduced using the proposed technique. [ABSTRACT FROM AUTHOR]

The paper demonstrates that the possibilities of producing reliable long-term energy forecast are limited. Global economic growth is so dynamic that the changes resulting from scientific and technological progress, which we experience, do not allow for the identification of goals over longer time intervals. For this reason, forecasting up to 2035, 2040, 2060 is devoid of its fundamental value, which is the reliability of results obtained. Are predictions to be conservative (this applies to richer countries) or are they to produce a paradigm shift by, for example, strongly imposing the requirement of increasing energy security, which is important for our country? In light of the broadness of the issues, this paper is limited to considerations relating to forecasting the primary energy demand. Detailed examples are presented for the Polish energy system. [ABSTRACT FROM AUTHOR]

Forecasting wind speed is a crucial issue in the wind power industry, and wind energy is gradually coming to be viewed as one of the most promising alternative energy sources because of its cleanliness and renewability for large-scale commercial production. With the aim of developing accurate tools for forecasting wind speed, this paper presents a novel hybrid intelligent forecasting model based on Least Square Support Vector Machine and the Markov model. Prior to conducting the wind speed forecasting, the original wind speed series is processed using the C–C method, which is a phase space reconstruction method, to automatically determine the input form. Subsequently, the LSSVM (least squares support vector machine) model, which is optimized using the PSOGSA (partical swarm optimization combined with gravitational search algorithm) algorithm, is employed to forecast wind speed. Additionally, the Markov model is developed to make error corrections with the state ranges determined using the FCM (fuzzy C-means) model. The proposed model takes the average forecasting values between the outputs generated by LSSVM and error corrected LSSVM model as the final forecasting results. The simulation results indicate that the proposed model can outperform the other models discussed in this paper with respect to forecasting accuracy and can be a suitable tool for forecasting wind speeds. [ABSTRACT FROM AUTHOR]

ABSTRACT Electricity demand is influenced by atmospheric conditions, and, therefore it is important to quantify their relationships suitably for accurate electricity demand forecasting and the implementation of power-saving policies. However, interdependencies and characteristics of covariance among meteorological variables within the same periodicities hinder the quantification of their direct and indirect impacts on electric power load. To investigate the strength of the direct correlation between atmospheric conditions and electric power load, this study harnessed a new partialization analysis method based on a partial phase synchronization index combined with wavelet transformation. The advantage of the proposed method is that it can be used to evaluate the degree of independent contribution of the variables over different spatiotemporal scales. Compared with traditional statistical analyses, this new partialization analysis shows that air temperature is the principal variable associated directly with electricity demand, but that the strength of the relationship varies with season and time scale. Relative humidity and wind speed have strong direct correlations with electricity in summer and winter, respectively. Insolation is directly coupled to the electric power load only on sub-diurnal time scales. This investigation indicates that for accurate forecasting of electricity demand, changes in the coupling strengths of different atmospheric variables should be incorporated into the electric power load forecasting process. The study shows that a partial phase synchronization index, combined with wavelet transformation, is a useful tool that could be used in other studies to assess complex interacting atmospheric oscillations that cannot be assessed properly by traditional approaches. [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 severe worldwide oil and gas demand is causing the petroleum exploration and production industry to search for challenging energy resources. Moreover, the increasing interest in natural gas has provided an opportunity for the development, research and advancement of technology to access unconventional reservoirs such as sour gas. Although many natural gases containing CO 2 and H 2 S have been discovered, reliable experimental data on the thermodynamic properties of these gases are very rare. There is practically no data existing on sour gas containing H 2 S because of its toxicity. This lack of information obviously affects estimations of transport properties such as viscosity. This study focuses on the estimation of sour gas properties such as gas compressibility and viscosity and evaluates the impact of viscosity, particularly on reservoir inflow performance and production forecasting. The well-known and industry-standard methods such as the Peng–Robinson (PR) and Soave-Redlich-Kwong (SRK) equations of state, the Dranchuk-Abu Kassem correlation with the Wichert-Aziz correction for non-hydrocarbons, and the GERG-2008 (Gas European Gas Research Group) equation of state were tested for compressibility estimation. For gas viscosity, several other methods, such as the model developed by the National Institute of Standards and Technology (NIST) and methods developed by Lohrenz, Bray and Clark; Pedersen et al.; Carr, Kobayashi and Burrows; Lee, Gonzalez and Eakin; Dean and Stiel; and Lucas, have been tested for gas viscosity estimation. Evaluations using these methods have been achieved with several compositions of gas samples at different pressures and temperatures. The results indicate that all of the methods provide acceptable estimations of gas density. However, all of the viscosity methods revealed deviations for all gases, particularly at high pressures and temperatures (HPHT). The margin of error was even greater for sour gases containing significant amounts of H 2 S. The results of the inflow performance relationship based on various methods of viscosity estimation show that the uncertainty in the estimated gas viscosities has a major effect on reservoir performance and production forecasting. [ABSTRACT FROM AUTHOR]

Yaw control systems orientate the rotor of a wind turbine into the wind direction, optimize the wind power generated by wind turbines and alleviate the mechanical stresses on a wind turbine. Regarding the advantages of yaw control systems, a k-nearest neighbor classifier (k-NN) has been developed in order to forecast the yaw position parameter at 10-min intervals in this study. Air temperature, atmosphere pressure, wind direction, wind speed, rotor speed and wind power parameters are used in 2, 3, 4, 5 and 6-dimensional input spaces. The forecasting model using Manhattan distance metric for k = 3 uncovered the most accurate performance for atmosphere pressure, wind direction, wind speed and rotor speed inputs. However, the forecasting model using Euclidean distance metric for k = 1 brought out the most inconsistent results for atmosphere pressure and wind speed inputs. As a result of multi-tupled analyses, many feasible inferences were achieved for yaw position control systems. In addition, the yaw position forecasting model developed was compared with the persistence model and it surpassed the persistence model significantly in terms of the improvement percent. [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]

Rising carbon emission or carbon footprint imposes grave concern over the earth׳s climatic condition, as it results in increasing average global temperature. Renewable energy sources seem to be the favorable solution in this regard. It can reduce the overall energy consumption rate globally. However, the renewable sources are intermittent in nature with very high initial installation price. Off-grid Small Autonomous Hybrid Power Systems (SAHPS) are good alternative for generating electricity locally in remote areas, where the transmission and distribution of electrical energy generated from conventional sources are otherwise complex, difficult and costly. In optimizing SAHPS, weather data over past several years are generally the main input, which include wind speed and solar radiation. The weather resources used in this optimization process have unsystematic variations based on the atmospheric and seasonal phenomenon and it also varies from year to year. While using past data in the analysis of SAHPS performance, it was assumed that the same pattern will be followed in the next year, which in reality is very unlikely to happen. In this paper, we use BBO optimization algorithm for SAHPS optimal component sizing by minimizing the cost of energy. We have also analysed the effect of using forecast weather data instead of past data on the SAHPS performance. ANNs, which are trained with back-propagation training algorithm, are used for wind speed and solar radiation forecasting. A case study was used for demonstrating the performance of BBO optimization algorithm along with forecasting effects. The simulation results clearly showed the advantages of utilizing wind speed and solar radiation forecasting in a SAHPS optimization problem. [ABSTRACT FROM AUTHOR]

The water sector in Algeria has to date paid scant attention to the issue of climate change and is often unaware of its impact on future water resources. Studies will be needed to assess the impact and cost of climate change and draw up adaptation solutions. Forecasts are not optimistic. Models for climate change indicate that rainfall could decrease by more than 20% by 2050, which would result in even greater worsening water shortages in different basins of Algeria. The construction of 70 dams planned will provide only small additional volumes. The particular challenge for Algeria in the coming decades will be to adapt to a decrease in renewable water resources. The country will have to carefully manage these resources. Mobilization of non-conventional water resources (desalination and wastewater reuse) will be a strategic component of future water policy. The development of unconventional resources and the management of water demand will increase more the energy consumption of the water sector. This consumption would reach nearly 12% of the country׳s consumption and must be integrated dice now in the country׳s energy forecasts. More coordinated planning and action will consequently be required between the water and energy sectors if further aggravation of the water deficit is to be avoided. Moreover, the revolution in renewable energy (wind and solar power) in terms of technological development and costs may help reduce the consumption of fossil fuels and ensure reserves for future generations by fostering decentralized renewable energy projects for alimentation of pumping stations. Algeria has thus set itself by 2030 a share of renewable energy in the national energy balance of between 30% and 40%. The share of renewable power by 2023 will represent about 17% of installed capacity (5539 MW) compared to 4.74% in 2011 (540 MW). [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]

Wind energy is regarded as a worldwide renewable and alternative energy that can relieve the energy shortage, reduce environmental pollution, and provide a significant potential economic benefit. In this paper, a hybrid method is developed to properly and efficiently forecast the daily wind speed in Hainan Province, China. The proposed hybrid forecasting model consists of outlier detection and a bivariate fuzzy time series, which provides a more powerful forecasting capacity of daily wind speed than that of traditional single forecasting models. To verify the developed approach, daily wind speed data from January 2008 to December 2012 in Hainan Province, China, are used for model construction and testing. The results show that the developed hybrid forecasting model achieves high forecasting accuracy and is suitable for forecasting the wind energy of China's large wind farms. [ABSTRACT FROM AUTHOR]

The article discusses the economic relations between Europe and Africa in relation to the document "Roadmap 2050: A Practice Guide to a Prosperous, Low-Carbon Europe" by the architecture firm Office of Metropolitan Architecture (OMA) through its research group AMO. The plan involves the energy region Enerope as a vision of the European Union (EU) as self-sufficient in energy resources, while using solar energy from North Africa. Other topics include the notion of North Africa as part of the Solaria region, mythological aspects of the plan, and the role of geography in the planning.

Abstract: The prediction of wind speed is one of the most important aspects when dealing with renewable energy. In this paper we show a new nonparametric model, based on semi-Markov chains, to predict wind speed and the energy produced by a commercial blade. Particularly, we use an indexed semi-Markov model, that reproduces accurately the statistical behavior of wind speed. The model is used to forecast future wind speed and the energy produced through a 10 kW Aircon wind turbine. We forecast one step ahead and for different time scales. In order to check the main features of the model we show, as indicator of goodness, the root mean square error between real data and predicted ones. We compare our forecasting results with those of a persistence model and of an autoregressive model. [Copyright &y& Elsevier]

Highlights: [•] We benchmark forecasting methods for hourly PV electricity production. [•] Forecasts are built with statistical methods without technical information about panels or irradiation models. [•] Our best model performs very well, is accurate and reliable. [Copyright &y& Elsevier]

Highlights: [•] Effective ANN-based models for forecasting the power produced by a LS-GCPV plant is presented. [•] A simple but accurate analytical expressions have been developed to forecast the power output. [•] The effectiveness of the split of the dataset into three different ones has been shown. [•] The better ability of ANN-models to forecast the power produced has been verified experimentally. [Copyright &y& Elsevier]