Remote sensing techniques for conservation and management of natural vegetation ecosystems

The importance of using remote sensing techniques, in the visible and near-infrared ranges, for mapping, inventory, conservation and management of natural ecosystems is discussed. Some examples realized in Brazil or other countries are given to evaluate the products from orbital platform (MSS and RBV imagery of LANDSAT) and aerial level (photography) for ecosystems study. The maximum quantitative and qualitative information which can be obtained from each sensor, at different level, are discussed. Based on the developed experiments it is concluded that the remote sensing technique is a useful tool in mapping vegetation units, estimating biomass, forecasting and evaluation of fire damage, disease detection, deforestation mapping and change detection in land-use. In addition, remote sensing techniques can be used in controling implantation and planning natural/artificial regeneration

Methodological approaches to accounting the depletion of natural resources, changes in the environmental and human capital in the gross regional product

A key indicator of the System of National Accounts of Russia at a regional scale is Gross Regional Product characterizing the value of goods and services produced in all sectors of the economy in a country and intended for final consumption, capital formation and net exports (excluding imports). From a sustainability perspective, the most weakness of GRP is that it ignores depreciation of man-made assets, natural resource depletion, environmental pollution and degradation, and potential social costs such as poorer health due to exposure to occupational hazards. Several types of alternative approaches to measuring socio-economic progress are considering for six administrative units of the Ural Federal District for the period 2006-2014. Proposed alternatives to GRP as a measure of social progress are focused on natural resource depletion, environmental externalities and some human development aspects. The most promising is the use of corrected macroeconomic indicators similar to the “genuine savings” compiled by the World Bank. Genuine savings are defined in this paper as net savings (net gross savings minus consumption of fixed capital) minus the consumption of natural non-renewable resources and the monetary evaluations of damages resulting from air pollution, water pollution and waste disposal. Two main groups of non renewable resources are considered: energy resources (uranium ore, oil and natural gas) and mineral resources (iron ore, copper, and aluminum). In spite of various shortcomings, this indicator represents a considerable improvement over GRP information. For example, while GRP demonstrates steady growth between 2006 and 2014 for the main Russian oil- and gas-producing regions – Hanty-Mansi and Yamalo-Nenets Autonomous Okrugs, genuine savings for these regions decreased over all period. It means that their resource-based economy could not be considered as being on a sustainable path even in the framework of “weak” sustainability, i.e. sustainability under the assumption that the accumulation of producible physical capital and of human capital can compensate for losses in natural non reproducible resources.The research has been supported by the Russian Science Foundation (Project № 14-18-00574 "Anti-crisis information-analytical system: diagnostics of regions, threat assessment and scenario forecasting in order to keep and to strengthen economic security and well-being of Russia")

Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression

Deep neural networks are proposed for short-term natural gas load forecasting. Deep learning has proven to be a powerful tool for many classification problems seeing significant use in machine learning fields such as image recognition and speech processing. We provide an overview of natural gas forecasting. Next, the deep learning method, contrastive divergence is explained. We compare our proposed deep neural network method to a linear regression model and a traditional artificial neural network on 62 operating areas, each of which has at least 10 years of data. The proposed deep network outperforms traditional artificial neural networks by 9.83% weighted mean absolute percent error (WMAPE)

Enhanced news sentiment analysis using deep learning methods

We explore the predictive power of historical news sentiments based on financial market performance to forecast financial news sentiments. We define news sentiments based on stock price returns averaged over one minute right after a news article has been released. If the stock price exhibits positive (negative) return, we classify the news article released just prior to the observed stock return as positive (negative). We use Wikipedia and Gigaword five corpus articles from 2014 and we apply the global vectors for word representation method to this corpus to create word vectors to use as inputs into the deep learning TensorFlow network. We analyze high-frequency (intraday) Thompson Reuters News Archive as well as the high-frequency price tick history of the Dow Jones Industrial Average (DJIA 30) Index individual stocks for the period between 1/1/2003 and 12/30/2013. We apply a combination of deep learning methodologies of recurrent neural network with long short-term memory units to train the Thompson Reuters News Archive Data from 2003 to 2012, and we test the forecasting power of our method on 2013 News Archive data. We find that the forecasting accuracy of our methodology improves when we switch from random selection of positive and negative news to selecting the news with highest positive scores as positive news and news with highest negative scores as negative news to create our training data set.Published versio

Collinsville solar thermal project: energy economics and dispatch forecasting (final report)

The primary aim of this report is to help negotiate a Power Purchase Agreement (PPA) for the proposed hybrid gas-Linear Frensel Reflector (LFR) plant at Collinsville, Queensland, Australia.  The report’s wider appeal is the discussion of the current situation in Australian National Electricity Market (NEM) and techniques and methods used to model the NEM’s demand and wholesale spot prices for the lifetime of the proposed plant. Executive Summary 1        Introduction This report primarily aims to provide both dispatch and wholesale spot price forecasts for the proposed hybrid gas-solar thermal plant at Collinsville, Queensland, Australia for its lifetime 2017-47.  These forecasts are to facilitate Power Purchase Agreement (PPA) negotiations and to evaluate the proposed dispatch profile in Table 3.  The solar thermal component of the plant uses Linear Fresnel Reflector (LFR) technology.  The proposed profile maintains a 30 MW dispatch during the weekdays by topping up the yield from the LFR by dispatch from the gas generator and imitates a baseload function currently provided by coal generators.  This report is the second of two reports and uses the findings of our first report on yield forecasting (Bell, Wild & Foster 2014b). 2        Literature review The literature review discusses demand and supply forecasts, which we use to forecast wholesale spot prices with the Australian National Electricity Market (ANEM) model. The review introduces the concept of gross demand to supplement the Australian Electricity Market Operator’s (AEMO) “total demand”.  This gross demand concept helps to explain the permanent transformation of the demand in the National Electricity Market (NEM) region and the recent demand over forecasting by the AEMO.  We also discuss factors causing the permanent transformation.  The review also discusses the implications of the irregular ENSO cycle for demand and its role in over forecasting demand. Forecasting supply requires assimilating the information in the Electricity Statement of Opportunities (ESO) (AEMO 2013a, 2014c).  AEMO expects a reserve surplus across the NEM beyond 2023-24.  Compounding this reserve surplus, there is a continuing decline in manufacturing, which is freeing up supply capacity elsewhere in the NEM.  The combined effect of export LNG prices and declining total demand are hampering decisions to transform proposed gas generation investment into actual investment and hampering the role for gas as a bridging technology in the NEM.  The review also estimates expected lower and upper bounds for domestic gas prices to determine the sensitivity of the NEM’s wholesale spot prices and plant’s revenue to gas prices. The largest proposed investment in the NEM is from wind generation but the low demand to wind speed correlation induces wholesale spot price volatility.  However, McKinsey Global Institute (MGI 2014) and Norris et al. (2014a) expect economically viable energy storage shortly beyond the planning horizon of the ESO in 2023-24.  We expect that this viability will not only defer investment in generation and transmission but also accelerate the growth in off-market produced and consumed electricity within the NEM region. 2.1     Research questions The report has the following overarching research questions: What is the expected dispatch of the proposed plant’s gas component given the plant’s dispatch profile and expected LFR yield? What are the wholesale spots prices on the NEM given the plant’s dispatch profile? The literature review refines the latter research question into five more specific research questions ready for the methodology: What are the half-hourly wholesale spots prices for the plant’s lifetime without gas as a bridging technology? Assuming a reference gas price of between $5.27/GJ to$7.19/GJ for base-load gas generation (depending upon nodal location;) and for peak-load gas generation of between $6.59/GJ to$8.99/GJ; and given the plant’s dispatch profile What are the half-hourly wholesale spots prices for the plant’s lifetime with gas as a bridging technology? Assuming some replacement of coal with gas generation How sensitive are wholesale spot prices to higher gas prices? Assuming high gas prices are between $7.79/GJ to$9.71/GJ for base-load gas generation (depending upon nodal location); and for peak-load gas generation of between $9.74/GJ to$12.14/GJ; and What is the plant’s revenue for the reference gas prices? How sensitive is the plant’s revenue to gas as a bridging technology? How sensitive is the plant’s revenue to the higher gas prices? What is the levelised cost of energy for the proposed plant? 3        Methodology In the methodology section, we discuss the following items: dispatch forecasting for the proposed plant; supply capacity for the years 2014-47 for the NEM; demand forecasting using a Typical Meteorological Year (TMY); and wholesale spot prices calculation using ANEM, supply capacity and total demand define three scenarios to address the research questions: reference gas prices; gas as a bridging technology; and high gas prices. The TMY demand matches the solar thermal plant’s TMY yield forecast that we developed in our previous report (Bell, Wild & Foster 2014b).  Together, these forecasts help address the research questions. 4        Results In the results section we will present the findings for each research question, including the TMY yield for the LFR and the dispatch of the gas generator given the proposed dispatch profile in Table 3; Average annual wholesale spot prices from 2017 to 2047 for the plant’s node for: Reference gas prices scenario from $18/MWh to$38/MWh Gas as a bridging technology scenario from $18/MWh to$110/MWh High gas price scenario from $20/MWh to$41/MWh The combined plants revenue without subsidy given the proposed profile: Reference gas price scenario $36 million Gas as a bridging technology scenario$52 million High gas price scenario $47 million 5        Discussion In the discussion section, we analyse: reasons for the changes in the average annual spot prices for the three scenarios; and the frequency that the half-hourly spot price exceeds the Short Run Marginal Cost (SRMC) of the gas generator for the three scenarios for: day of the week; month of the year; and time of the day. If the wholesale spot price exceeds the SRMC, dispatch from the gas plant contributes towards profits.  Otherwise, the dispatch contributes towards a loss.  We find that for both reference and high gas price scenarios the proposed profile in Table 3 captures exceedances for the day of the week and the time of the day but causes the plant to run at a loss for several months of the year.  Figure 14 shows that the proposed profile captures the exceedance by hour of the day and Figure 16 shows that only operating the gas component Monday to Friday is well justified.  However, Figure 15 shows that operating the gas plant in April, May, September and October is contributing toward a loss.  Months either side of these four months have a marginal number of exceedances.  In the unlikely case of gas as a bridging scenario, extending the proposed profile to include the weekend and operating from 6 am to midnight would contribute to profits. We offer an alternative strategy to the proposed profile because the proposed profile in the most likely scenarios proves loss making when considering the gas component’s operation throughout the year.  The gas-LFR plant imitating the based-load role of a coal generator takes advantage of the strengths of the gas and LFR component, that is, the flexibility of gas to compensate for the LFR’s intermittency, and utilising the LFR’s low SRMC.  However, the high SRMC of the gas component in a baseload role loses the flexibility to respond to market conditions and contributes to loss instead of profit and to CO2 production during periods of low demand. The alternative profile retains the advantages of the proposed profile but allows the gas component freedom to exploit market conditions.  Figure 17 introduces the perfect day’s yield profile calculated from the maximum hourly yield from the years 2007-13.  The gas generator tops up the actual LFR yield to the perfect day’s yield profile to cover LFR intermittency.  The residual capacity of the gas generator is free to meet demand when spot market prices exceed SRMC and price spikes during Value-of-Lost-Load (VOLL) events.  The flexibility of the gas component may prove more advantageous as the penetration of intermittent renewable energy increases. 6        Conclusion We find that the proposed plant is a useful addition to the NEM but the proposed profile is unsuitable because the gas component is loss making for four months of the year and producing CO2 during periods of low demand.  We recommend further research using the alternative perfect day’s yield profile. 7        Further Research We discuss further research compiled from recommendation elsewhere in the report. 8        Appendix A Australian National Electricity Market Model Network This appendix provides diagrams of the generation and load serving entity nodes and the transmission lines that the ANEM model uses.  There are 52 nodes and 68 transmission lines, which make the ANEM model realistic.  In comparison, many other models of the NEM are highly aggregated. 9        Appendix B Australian National Electricity Market Model This appendix describes the ANEM model in detail and provides additional information on the assumptions made about the change in the generation fleet in the NEM during the lifetime of the proposed plant