Evaluating penalized logistic regression models to predict Heat-Related Electric grid stress days

Bramer, Lisa; Rounds, Jeremiah; Burleyson, Casey D.; Fortin, Daniel C.; Hathaway, John E.; Rice, Jennie S.; Kraucunas, Ian

doi:10.1016/j.apenergy.2017.09.087

Cited by 9 publications

(6 citation statements)

References 25 publications

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“…The study in [23] was almost equivalent to the proposed model presented in [22]. Moreover, the MAPE achieved in this study was between 1.23% to 3.35% in seven different states of America [34].…”

Section: Related Worksupporting

confidence: 59%

Game Theoretical Demand Response Management and Short-Term Load Forecasting by Knowledge Based Systems on the basis of Priority Index

et al. 2018

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Demand Response Management (DRM) is considered one of the crucial aspects of the smart grid as it helps to lessen the production cost of electricity and utility bills. DRM becomes a fascinating research area when numerous utility companies are involved and their announced prices reflect consumer’s behavior. This paper discusses a Stackelberg game plan between consumers and utility companies for efficient energy management. For this purpose, analytical consequences (unique solution) for the Stackelberg equilibrium are derived. Besides this, this paper presents a distributed algorithm which converges for consumers and utilities. Moreover, different power consumption activities on the basis of time series are becoming a basic need for load prediction in smart grid. Load forecasting is taken as the significant concerns in the power systems and energy management with growing technology. The better precision of load forecasting minimizes the operational costs and enhances the scheduling of the power system. The literature has discussed different techniques for demand load forecasting like neural networks, fuzzy methods, Naïve Bayes, and regression based techniques. This paper presents a novel knowledge based system for short-term load forecasting. The algorithms of Affinity Propagation and Binary Firefly Algorithm are integrated in knowledge based system. Besides, the proposed system has minimum operational time as compared to other techniques used in the paper. Moreover, the precision of the proposed model is improved by a different priority index to select similar days. The similarity in climate and date proximity are considered all together in this index. Furthermore, the whole system is distributed in sub-systems (regions) to measure the consequences of temperature. Additionally, the predicted load of the entire system is evaluated by the combination of all predicted outcomes from all regions. The paper employs the proposed knowledge based system on real time data. The proposed scheme is compared with Deep Belief Network and Fuzzy Local Linear Model Tree in terms of accuracy and operational cost. In addition, the presented system outperforms other techniques used in the paper and also decreases the Mean Absolute Percentage Error (MAPE) on a yearly basis. Furthermore, the novel knowledge based system gives more efficient outcomes for demand load forecasting.

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Section: Related Worksupporting

confidence: 59%

Game Theoretical Demand Response Management and Short-Term Load Forecasting by Knowledge Based Systems on the basis of Priority Index

et al. 2018

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“…(11) Regional gross domestic product (GDP), LE 11 , which reflects the overall level of regional economic development. (12) Tertiary industry as a proportion of GDP, LE 12 , which reflects the advanced level of regional economic development. (13) Total retail sales of consumer goods, LE 13 , which measures the changes in regional retail market and the prosperity of regional economy.…”

Section: Grey Relational Analysis and Coupling Development Evaluationmentioning

confidence: 99%

“…Logistics heat has always been a hot topic in the research of the logistics industry [11][12][13][14][15]. Lan and Zhong [16] carried out entropy analysis and classification of the data collected from online logistics heat maps, aiming to optimize the spatial pattern of logistics in economic development zones.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Regional Logistics Heat and Coupling Development between Regional Logistics and Economic Systems

Yin

Peng

2021

Discrete Dynamics in Nature and Society

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The research on logistics heat facilitates the understanding of the drivers of regional logistics development. However, many scholars ignore the difference between prediction methods in terms of attributes and focal points of data analysis during the selection of regional logistics heat prediction model. Regional logistics interacts with regional economy. However, the studies on the coupling development between the two systems fail to make a detailed analysis in the light of their actual situation. Therefore, the evaluation of the coordination degree is often biased. To solve the problem, this paper probes into the prediction of regional logistics heat and the coupling development between regional logistics and economic systems. Firstly, an index system was established to measure the level of coupling development between the two systems, and a grey relational analysis was performed on the indices, leading to the evaluation results on coordination degree. Next, a composite model of GM (1, 1) and backpropagation (BP) neural network was proposed, and the deviation interval of the composite predictions was predicted based on Markov chain prediction model. The proposed algorithm proved effective through experiments.

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“…Electricity demand has a strong nonlinear dependence on ambient air temperature (Bramer et al 2017, Wang and Bielicki 2018, Fonseca et al 2019: increased air temperatures tend to increase demand for air conditioning except at low temperatures, when increased air temperatures reduce demand for electric heating. To capture this nonlinear relationship, we estimate hourly electricity demand (D (MWh)) as a function of air temperature (T (°C)) using a piecewise linear regression model that enforces continuities at its breakpoints (see SI.2.3) (Fonseca et al 2019):…”

Section: Electricity Demandmentioning

confidence: 99%

Compounding climate change impacts during high stress periods for a high wind and solar power system in Texas

Craig

Jaramillo

Hodge

et al. 2020

Environ. Res. Lett.

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Power system planning aims at ensuring that sufficient supply-and demand-side assets exist to meet electricity demand at all times. For a Texas electric power system with high wind and solar penetrations, we quantify how climate change will affect supply and demand during three types of high stress periods for the power grid: high demand hours, high net demand hours, and high system ramp hours. We specifically quantify effects on demand, reductions in available thermal capacity (i.e. thermal deratings), wind and solar generation, and net demand. We estimate each using meteorological variables from five climate change projections (2041-2050) assuming Representative Concentration Pathway 8.5 and from a reference period (1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005). All five projections indicate that climate change will increase demand by up to 2 GWh during high demand hours (4% of demand in the reference period) and increase net demand by up to 3 GWh during high net demand periods (6% of net demand in the reference period). All five projections also indicate thermal deratings will increase during high demand and net demand periods by up to 2 GWh and high net demand ramps will increase by up to 2 GW. Overall, our results indicate compounding effects of climate change in Texas will necessitate greater investment in peak and flexible capacity. RECEIVED

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Evaluating penalized logistic regression models to predict Heat-Related Electric grid stress days

Cited by 9 publications

References 25 publications

Game Theoretical Demand Response Management and Short-Term Load Forecasting by Knowledge Based Systems on the basis of Priority Index

Game Theoretical Demand Response Management and Short-Term Load Forecasting by Knowledge Based Systems on the basis of Priority Index

Prediction of Regional Logistics Heat and Coupling Development between Regional Logistics and Economic Systems

Compounding climate change impacts during high stress periods for a high wind and solar power system in Texas

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