Forecasting accuracy electricity load can help industrial enterprises optimise production scheduling based on peak and off-peak electricity prices. The electricity load forecasting results can be ...provided to an electricity system to improve electricity generation efficiency and minimize energy consumption by developing electricity generation plans in advance and by avoiding over or under the generation of electricity. However, because of the different informatization levels in different industries, few reliable intelligent electricity management systems are applied on the power supply side. Based on industrial big data and machine learning algorithms, this study proposes an integrated model to forecast short-term electricity load. The hybrid model based on the hybrid mode decomposition algorithms is proposed to decompose the total electricity load signal. To improve the generalisation ability of the forecasting model, a dynamic forecasting model is proposed based on the improved hybrid intelligent algorithm to forecast the short-term electricity load. The results show that the accuracy of the proposed dynamic integrated electricity load forecasting model is as high as 99%. The integrated framework could forecast abnormal electricity consumption in time and provide reliable evidence for production process scheduling.
•Forecasting models are too difficult to embedded in industrial process systems.•The novel integrated framework has been proposed for industrial processes.•The proposed integrated model has been implemented in papermaking enterprise.•The accuracy of the proposed dynamic load forecasting model is greater than 99%.•The proposed energy management system framework is conducive to green production.
Changing Texas Murdock, Steve H; Cline, Michael E; Zey, Mary A ...
2014, 2013, 2014-02-28, 20140101
eBook
<!CDATA Drawing on nearly thirty years of prior analyses of growth, aging, and diversity in Texas populations and households, the authors of Changing Texas: Implications of Addressing or Ignoring the ...Texas Challenge examine key issues related to future Texas population change and its socioeconomic implications. Current interpretation of data indicates that, in the absence of any change in the socioeconomic conditions associated with the demographic characteristics of the fastest growing populations, Texas will become poorer and less competitive in the future. However, the authors delineate how such a future can be altered so that the “Texas Challenge” becomes a Texas advantage, leading to a more prosperous future for all Texans. Presenting extensive data and projections for the period through 2050, Changing Texas permits an educated preview of Texas at the middle of the twenty-first century. Discussing in detail the implications of population-related change and examining how the state could alter those outcomes through public policy, Changing Texas offers important insights for the implications of Texas’ changing demographics for educational infrastructure, income and poverty, unemployment, healthcare needs, business activity, public funding, and many other topics important to the state, its leaders, and its people. Perhaps most importantly, Changing Texas shows how objective information, appropriately analyzed, can inform governmental and private-sector policies that will have important implications for the future of Texas. >
Incorporating solar energy into a grid necessitates an accurate power production forecast for photovoltaic (PV) facilities. In this research, output PV power was predicted at an hour ahead on yearly ...basis for three different PV plants based on polycrystalline (p-si), monocrystalline (m-si), and thin-film (a-si) technologies over a four-year period. Wind speed, module temperature, ambiance, and solar irradiation were among the input characteristics taken into account. Each PV plant power output was the output parameter. A deep learning method (RNN-LSTM) was developed and evaluated against existing techniques to forecast the PV output power of the selected PV plant. The proposed technique was compared with regression (GPR, GPR (PCA)), hybrid ANFIS (grid partitioning, subtractive clustering and FCM) and machine learning (ANN, SVR, SVR (PCA)) methods. Furthermore, different LSTM structures were also investigated, with recurrent neural networks (RNN) based on 2019 data to determine the best structure. The following parameters of prediction accuracy measure were considered: RMSE, MSE, MAE, correlation (r) and determination (R2) coefficients. In comparison to all other approaches, RNN-LSTM had higher prediction accuracy on the basis of minimum (RMSE and MSE) and maximum (r and R2). The p-si, m-si and a-si PV plants showed the lowest RMSE values of 26.85 W/m2, 19.78 W/m2 and 39.2 W/m2 respectively. Moreover, the proposed method was found to be robust and flexible in forecasting the output power of the three considered different photovoltaic plants.
Investor sentiment plays an important role on the stock market. User-generated textual content on the Internet provides a precious source to reflect investor psychology and predicts stock prices as a ...complement to stock market data. This paper integrates sentiment analysis into a machine learning method based on support vector machine. Furthermore, we take the day-of-week effect into consideration and construct more reliable and realistic sentiment indexes. Empirical results illustrate that the accuracy of forecasting the movement direction of the SSE 50 Index can be as high as 89.93% with a rise of 18.6% after introducing sentiment variables. And, meanwhile, our model helps investors make wiser decisions. These findings also imply that sentiment probably contains precious information about the asset fundamental values and can be regarded as one of the leading indicators of the stock market.
•Comparison of different EMS in terms of cost benefit for a PV/ battery system in a microgrid.•Battery ageing model implementation in simulation.•Influence of the battery cost on the strategy’s ...performances.
Several energy management strategies, of increasing complexity, are compared in term of cost benefit for a Photovoltaic /battery system providing electricity to an accommodation building with an electric vehicle. Rule based control methods with or without production forecasting are implemented and compared to a linear programming strategy used as a reference. The improvement in term of gain between simplest and reference methods is about 27%. It appears that the battery cycles number differs greatly (up to 55%), leading to a more or less rapid ageing. Battery degradation models are thus added and a corresponding cost is introduced in the strategy benefit. The results, depending on the initial battery cost, are significantly impacted, changing the relevance of the control strategies.
The intra-day time-varying pattern of solar data is more informative than the aggregated mean daily data. However, most of the traditional forecasting models often construct the 1-day ahead daily ...power forecast based on its historical daily averages but ignore the information from its intra-day dynamic pattern. Intuitively, the use of aggregated data could cause certain loss of information in forecasting, which in turn adversely affects forecasting accuracy. In order to make use of the valuable trajectory information of the power output within a day, this paper suggests a partial functional linear regression model (PFLRM) for forecasting the daily power output of PV systems. The PFLRM is a generalization of the traditional multiple linear regression model but enables to model nonlinearity structure. Compared to the neural network models that are often criticized by the requirements of past experience and reliable knowledge in the design of network architecture, the suggested method only involves a few parameter estimates. A regularized algorithm was used to estimate the PFLRM parameters. It is shown that the regularized PFLRM improves the forecast accuracy of power output over the traditional multiple linear regression and neural network models. The results were validated based on a 2.1 kW grid connected PV system.
•Suggest a new model based on partial functional linear regression to predict solar output power.•The suggested model can incorporate the intra-day time varying pattern of output power into forecasting.•The comparison results favor the new model.
•Data augmentation techniques are applied to energy forecasting.•Data-oriented augmentations slightly outperform physics-oriented ones.•An encoder-decoder seq2seq deep learning wind power forecasting ...model is developed.•Proposed model’s superiority is validated on wind turbines in Arctic diverse terrains.•The methodology has potential applicability in other energy sectors.
Accurate wind power forecasting plays a critical role in the operation of wind parks and the dispatch of wind energy into the power grid. With excellent automatic pattern recognition and nonlinear mapping ability for big data, deep learning is increasingly employed in wind power forecasting. However, salient realities are that in-situ measured wind data are relatively expensive and inaccessible and correlation between steps is omitted in most multistep wind power forecasts. This paper is the first time that data augmentation is applied to wind power forecasting by systematically summarizing and proposing both physics-oriented and data-oriented time-series wind data augmentation approaches to considerably enlarge primary datasets, and develops deep encoder-decoder long short-term memory networks that enable sequential input and sequential output for wind power forecasting. The proposed augmentation techniques and forecasting algorithm are deployed on five turbines with diverse topographies in an Arctic wind park, and the outcomes are evaluated against benchmark models and different augmentations. The main findings reveal that on one side, the average improvement in RMSE of the proposed forecasting model over the benchmarks is 33.89%, 10.60%, 7.12%, and 4.27% before data augmentations, and increases to 40.63%, 17.67%, 11.74%, and 7.06%, respectively, after augmentations. The other side unveils that the effect of data augmentations on prediction is intricately varying, but for the proposed model with and without augmentations, all augmentation approaches boost the model outperformance from 7.87% to 13.36% in RMSE, 5.24% to 8.97% in MAE, and similarly over 12% in QR90. Finally, data-oriented augmentations, in general, are slightly better than physics-driven ones.
The El Niño Southern Oscillation (ENSO) is one of the most prominent interannual climate phenomena. Early and reliable ENSO forecasting remains a crucial goal, due to its serious implications for ...economy, society, and ecosystem. Despite the development of various dynamical and statistical prediction models in the recent decades, the “spring predictability barrier” remains a great challenge for long-lead-time (over 6 mo) forecasting. To overcome this barrier, here we develop an analysis tool, System Sample Entropy (SysSampEn), to measure the complexity (disorder) of the system composed of temperature anomaly time series in the Niño 3.4 region. When applying this tool to several near-surface air temperature and sea surface temperature datasets, we find that in all datasets a strong positive correlation exists between the magnitude of El Niño and the previous calendar year’s SysSampEn (complexity). We show that this correlation allows us to forecast the magnitude of an El Niño with a prediction horizon of 1 y and high accuracy (i.e., root-mean-square error = 0.23° C for the average of the individual datasets forecasts). For the 2018 El Niño event, our method forecasted a weak El Niño with a magnitude of 1.11 ± 0.23° C. Our framework presented here not only facilitates long-term forecasting of the El Niño magnitude but can potentially also be used as a measure for the complexity of other natural or engineering complex systems.
•A fused speed prediction method is devised based on Markov chain and neural network.•The inputs of neural network are simplified by principal component analysis.•The weights and threshold of neural ...network is optimized by genetic algorithm.•The predicted speed is validated effective in improving operating economy of vehicle.
Prediction of short-term future driving conditions can contribute to energy management of plug-in hybrid electric vehicles and subsequent improvement of their fuel economy. In this study, a fused short-term forecasting model for driving conditions is established by incorporating the stochastic forecasting and machine learning. The Markov chain is applied to calculate the transition probability of historical driving data, by which the stochastic prediction is conducted based on the Monte Carlo algorithm. Then, a neural network is employed to learn the current driving information and main knowledge after the simplified correlation of characteristic parameters, and meanwhile the genetic algorithm is adopted to optimize the initial weight and thresholds of networks. Finally, the short-term velocity prediction is achieved by combining them, and the overall performance is evaluated by four typical criteria. Simulation results indicate that the proposed fusion algorithm outperforms the single Markov model, the radial basis function neural network and the back propagation neural network with respect to the prediction precision and the difference distribution between expectation and prediction values. In addition, a case study is conducted by applying the built prediction algorithm in energy management of a plug-in hybrid electric vehicle, and simulation results highlight that the proposed algorithm can supply preferable velocity prediction, thereby facilitating improvement of the operating economy of the vehicle.
Unit Commitment With Gas Network Awareness Byeon, Geunyeong; Van Hentenryck, Pascal
IEEE transactions on power systems,
2020-March, 2020-3-00, 20200301, Volume:
35, Issue:
2
Journal Article
Peer reviewed
Open access
Recent changes in the fuel mix for electricity generation and, in particular, the increase in Gas-Fueled Power Plants (GFPP), have created significant interdependencies between the electrical power ...and natural gas transmission systems. However, despite their physical and economic couplings, these networks are still operated independently, with asynchronous market mechanisms. This mode of operation may lead to significant economic and reliability risks in congested environments as revealed by the 2014 polar vortex event experienced by the northeastern United States. To mitigate these risks, while preserving the current structure of the markets, this paper explores the idea of introducing gas network awareness into the standard unit commitment model. Under the assumption that the power system operator has some (or full) knowledge of gas demand forecast and the gas network, the paper proposes a tri-level mathematical program where natural gas zonal prices are given by the dual solutions of natural-gas flux conservation constraints and commitment decisions are subject to bid-validity constraints that ensure the economic viability of the committed GFPPs. This tri-level program can be reformulated as a single-level Mixed-Integer Second-Order Cone program which can then be solved using a dedicated Benders decomposition. The approach is validated on a case study for the Northeastern United States 1 that can reproduce the gas and electricity price spikes experienced during the early winter of 2014. The results on the case study demonstrate that gas awareness in unit commitment is instrumental in avoiding the peaks in electricity prices while keeping the gas prices to reasonable levels.
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