Accurate and timely spatial classification of crop types based on remote sensing data is important for both scientific and practical purposes. Spatially explicit crop-type information can be used to ...estimate crop areas for a variety of monitoring and decision-making applications such as crop insurance, land rental, supply-chain logistics, and financial market forecasting. However, there is no publically available spatially explicit in-season crop-type classification information for the U.S. Corn Belt (a landscape predominated by corn and soybean). Instead, researchers and decision-makers have to wait until four to six months after harvest to have such information from the previous year. The state-of-the-art research on crop-type classification has been shifted from relying on only spectral features of single static images to combining together spectral and time-series information. While Landsat data have a desirable spatial resolution for field-level crop-type classification, the ability to extract temporal phenology information based on Landsat data remains a challenge due to low temporal revisiting frequency and inevitable cloud contamination. To address this challenge and generate accurate, cost-effective, and in-season crop-type classification, this research uses the USDA's Common Land Units (CLUs) to aggregate spectral information for each field based on a time-series Landsat image data stack to largely overcome the cloud contamination issue while exploiting a machine learning model based on Deep Neural Network (DNN) and high-performance computing for intelligent and scalable computation of classification processes. Experiments were designed to evaluate what information is most useful for training the machine learning model for crop-type classification, and how various spatial and temporal factors affect the crop-type classification performance in order to derive timely crop type information. All experiments were conducted over Champaign County located in central Illinois, and a total of 1322 Landsat multi-temporal scenes including all the six optical spectral bands spanning from 2000 to 2015 were used. Computational experiments show the inclusion of temporal phenology information and evenly distributed spatial training samples in the study domain improves classification performance. The shortwave infrared bands show notably better performance than the widely used visible and near-infrared bands for classifying corn and soybean. In comparison with USDA's Crop Data Layer (CDL), this study found a relatively high Overall Accuracy (i.e. the number of the corrected classified fields divided by the number of the total fields) of 96% for classifying corn and soybean across all CLU fields in the Champaign County from 2000 to 2015. Furthermore, our approach achieved 95% Overall Accuracy by late July of the concurrent year for classifying corn and soybean. The findings suggest the methodology presented in this paper is promising for accurate, cost-effective, and in-season classification of field-level crop types, which may be scaled up to large geographic extents such as the U.S. Corn Belt.
•An in-season, field-level classification system for corn and soybean is presented.•Time series information of Landsat and the field boundary from CLU are used.•A case study has been demonstrated at a county in the US Corn Belt for 2000–2015.•Shortwave infrared bands provide most useful info for classifying corn and soybean.•95% classification accuracy can be achieved by late July of the concurrent year.
As a result of the analysis of the production and status of the fleet of flax harvesting machinery, negative problems with the machines of farms of different types were revealed. The extensive ...statistical material on the dynamics of changes in flax production indicators has been generalized. Conditional (reference) coefficients have been investigated and refined. The technological need for specialized flax harvesting equipment and its forecast for meeting Russia’s demand for flax fiber have been determined.
The technological advancement in the communication and control infrastructure helps those smart households (SHs) that more actively participate in the incentive-based demand response (IBDR) programs. ...As the agent facilitating the SHs' participation in the IBDR program, load aggregators (LAs) need to comprehend the available SHs' demand response (DR) capacity before trading in the day-ahead market. However, there are few studies that forecast the available aggregated DR capacity from LAs' perspective. Therefore, this article proposes a forecasting model aiming to aid LAs forecast the available aggregated SHs' DR capacity in the day-ahead market. First, a home energy management system is implemented to perform optimal scheduling for SHs and to model the customers' responsive behavior in the IBDR program; second, a customer baseline load estimation method is applied to quantify the SHs' aggregated DR capacity during DR days; third, several features which may have significant impacts on the aggregated DR capacity are extracted and they are processed by principal component analysis; and finally, a support vector machine based forecasting model is proposed to forecast the aggregated SHs' DR capacity in the day-ahead market. The case study indicates that the proposed forecasting framework could provide good performance in terms of stability and accuracy.
Distributed renewable energy, particularly photovoltaics (PV), has expanded rapidly over the past decade. Distributed PV is located behind the meter and is, thus, invisible to the retailers and the ...distribution system operator. This invisible generation, thus, injects additional uncertainty in the net load and makes it harder to forecast. This paper proposes a data-driven probabilistic net load forecasting method specifically designed to handle a high penetration of behind-the-meter (BtM) PV. The capacity of BtM PV is first estimated using a maximal information coefficient based correlation analysis and a grid search. The net load profile is then decomposed into three parts (PV output, actual load, and residual) which are forecast individually. Correlation analysis based on copula theory is conducted on the distributions and dependencies of the forecasting errors to generate a probabilistic net load forecast. Case studies based on ISO New England data demonstrate that the proposed method outperforms other approaches, particularly when the penetration of BtM PV is high.
Uncertainty modeling of renewable energy sources, load demand, electricity price, etc. create a high volume of data in smart grids. Accordingly, in this article, a precise forecasting method based on ...a deep learning concept with microclustering (MC) task is presented. The MC method is structured based on hybrid unsupervised and supervised clustering tasks by <inline-formula><tex-math notation="LaTeX">K</tex-math></inline-formula>-means and Gaussian support vector machine, respectively. In the proposed method, the input data sequence is clustered by the MC task, and then the forecasting process is employed. By applying the MC, input data in each hour are categorized into different groups, and a distinctive forecasting unit is allocated to each one. In this way, more clusters and forecasting networks are earmarked for the hours with higher fluctuation rates. The bi-directional long short-term memory (B-LSTM), which is one of the newest recurrent artificial neural networks, is proposed as the forecasting unit. The B-LSTM has bidirectional memory-feedforward and feedback loops-that helps us to investigate both previous and future hidden layers data. The optimal number of clusters in each hour is determined based on the Davies-Bouldin index. To evaluate the performance of the proposed method, in this study, three forecasting tasks including the wind speed, load demand, and electricity price are studied in different periods using the Ontario province, Canada, data set. The results are compared with other benchmarking methods to verify the robustness and effectiveness of the proposed method. In fact, the proposed method, which is equipped with the MC technique and B-LSTM networks, significantly promotes the forecasting results, especially in spike points.
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•A novel residual-based convolutional neural network model is developed for efficient wind power forecasting.•Variational mode decomposition contributes significantly to the forecast ...performance of the network.•The proposed network has both less complexity and less computational cost.•The proposed model provides superior short-term wind power forecasting performance.•The effectiveness of the method is compared with state-of-the-arts pre-trained networks.
An accurate forecast of wind power is very important in terms of economic dispatch and the operation of power systems. However, effectively mitigating the risks arising from wind power in power system operations greatly reduces the risk of wind energy producers, exposing them to potential additional costs. Being aware of this challenge, we introduced a two-step novel deep learning method for wind power forecasting. The first stage includes processes of Variational Mode Decomposition (VMD)-based feature extraction and converting these features into images. In the second stage, an improved residual-based deep Convolutional Neural Network (CNN) was utilized to forecast wind power. Meteorological wind speed, wind direction, and wind power data, which are directly related to each other, were employed as a dataset. The combined dataset was procured from a wind farm in Turkey between January 1 and December 31, 2018. The results of the proposed method were compared with the results obtained from the state-of-the-art deep learning architectures namely SqueezeNet, GoogLeNet, ResNet-18, AlexNet, and VGG-16 as well as physical model based on available meteorological forecast data. The proposed method outperformed the other architectures and demonstrated promising results for very short-term wind power forecasting due to its competitive performance.
In the last decades, the world's energy consumption has increased rapidly due to fundamental changes in the industry and economy. In such terms, accurate demand forecasts are imperative for decision ...makers to develop an optimal strategy that includes not only risk reduction, but also the betterment of the economy and society as a whole. This paper expands the fields of application of combined Bootstrap aggregating (Bagging) and forecasting methods to the electric energy sector, a novelty in literature, in order to obtain more accurate demand forecasts. A comparative out-of-sample analysis is conducted using monthly electric energy consumption time series from different countries. The results show that the proposed methodologies substantially improve the forecast accuracy of the demand for energy end-use services in both developed and developing countries. Findings and policy implications are further discussed.
•Electricity demand across different countries is forecasted 24 months in advance.•The potential gains of using bagging techniques to enhance forecasts are explored.•A new variation of a bagging procedure is proposed.•The proposed techniques provided consistently accurate forecasts in most cases.
Ocean warming and acidification threaten the future growth of coral reefs. This is because the calcifying coral reef taxa that construct the calcium carbonate frameworks and cement the reef together ...are highly sensitive to ocean warming and acidification. However, the global-scale effects of ocean warming and acidification on rates of coral reef net carbonate production remain poorly constrained despite a wealth of studies assessing their effects on the calcification of individual organisms. Here, we present global estimates of projected future changes in coral reef net carbonate production under ocean warming and acidification. We apply a meta-analysis of responses of coral reef taxa calcification and bioerosion rates to predicted changes in coral cover driven by climate change to estimate the net carbonate production rates of 183 reefs worldwide by 2050 and 2100. We forecast mean global reef net carbonate production under representative concentration pathways (RCP) 2.6, 4.5, and 8.5 will decline by 76, 149, and 156%, respectively, by 2100. While 63% of reefs are projected to continue to accrete by 2100 under RCP2.6, 94% will be eroding by 2050 under RCP8.5, and no reefs will continue to accrete at rates matching projected sea level rise under RCP4.5 or 8.5 by 2100. Projected reduced coral cover due to bleaching events predominately drives these declines rather than the direct physiological impacts of ocean warming and acidification on calcification or bioerosion. Presently degraded reefs were also more sensitive in our analysis. These findings highlight the low likelihood that the world's coral reefs will maintain their functional roles without near-term stabilization of atmospheric CO
emissions.
Short-term load forecasting is of major interest for the restructured environment of the electricity market. Accurate load forecasting is essential for effective power system operation, but ...electricity load is non-linear with a high level of volatility. Predicting such complex signals requires suitable prediction tools. This paper proposes a hybrid forecast strategy including novel feature selection technique, and a complex forecast engine based on a new intelligent algorithm. The electricity load signal is first filtered by feature selection technique to select appropriate candidates as input for the forecast engine. Then, the proposed two stage forecast engine is implemented based on ridgelet and Elman neural networks. All forecast engine parameters are chosen based on a novel intelligent algorithm to improve its accuracy and capability. Different electricity markets were considered as test cases to compare the proposed method with several current algorithms. Additionally, the proposed forecasting model measures the absolute forecasting errors in this work (among seven types of measurements i.e., absolute forecasting errors, measures based on percentage errors, symmetric errors, measures based on relative errors, scaled errors, relative measures and other error measures). The results validate the effectiveness of the proposed method.
•Presenting a new feature selection.•Application of two-stage forecast engine consists of RNN and ENN.•Presenting an improved stochastic algorithm for forecast engine optimization.•An effective prediction model for training the hybrid forecast engine is presented.•Testing forecast strategy over three real world engineering cases.
Brazil Is the New America Davidson, James Dale
2012, 2012., 2012-08-07T00:00:00, 2012-07-16, 2012-07-31
eBook
Look to Brazil for safe, stable investmentsAs the future of the American economy seems to get bleaker by the day, it is tempting to look abroad for business opportunities. Europe and Asia don't ...provide much hope, but what about somewhere that's both closer to home and sunny year-round? In Brazil is the New America: How Brazil Offers Upward Mobility in a Collapsing World,James D. Davidson shows that the current financial situation in Brazil is a haven for those looking to make money in a world in turmoil.With a population just 62 percent the size of that of the US, Brazil has added 15,023,633 jobs over the past eight years, while the US has lost millions. In a world burdened by bankrupt governments and aging populations, Brazil is solvent, with two people of working age for every dependent. In a world of 'Peak Oil' Brazil is energy independent, with 70 billion barrels of oil, 60% of the world's unused arable land, and 15% of its fresh water. Comparatively non-leveraged—and with significant room for growth and expansion, as well as vast natural resources, Brazil is a haven of opportunity.Written by James D. Davidson, the editor/publisher of Strategic Investmentand cofounder of Agora and the media outlet, Newsmax, Brazil is the New America details:How the original 'America' now embodies the brightest hope for realizing the American Dream while the 'Old America' is headed for a dramatic decline in the standard of livingInvestment opportunities not only for those willing to relocate, but anyone who can consider investing thereThe cost structure of employment in Brazil versus the United StatesBrazil has already learned its lesson about the dangers of inflation. Cash has taken the place of credit, and high interest rate returns are now the norm.
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