When to choose the simple average in forecast combination

Blanc, Sebastian M.; Setzer, Thomas

doi:10.1016/j.jbusres.2016.05.013

Cited by 30 publications

(13 citation statements)

References 31 publications

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“…For a user who has regular power consumption, the simple averaging (SA) strategies have also been shown to be highly competitive in applications [35]. In this work, the average of load power in last 7 days at the same time was chosen as the load power prediction data.…”

Section: User Load Power Predictionmentioning

confidence: 99%

Machine Learning Based Optimization Model for Energy Management of Energy Storage System for Large Industrial Park

Gao

Hong

2021

Processes

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Renewable energy represented by wind energy and photovoltaic energy is used for energy structure adjustment to solve the energy and environmental problems. However, wind or photovoltaic power generation is unstable which caused by environmental impact. Energy storage is an important method to eliminate the instability, and lithium batteries are an increasingly mature technique. If the capacity is too large, it would cause waste and cost would increase, but too small capacity cannot schedule well. At the same time, the size of energy storage capacity is also constrained by power consumption, whereas large-scale industrial power consumption is random and non-periodic. This is a complex problem which needs a model that can not only dispatch but also give a reasonable storage capacity. This paper proposes a model considering the cycle life of a lithium battery and the installation parameters of the battery, and the electricity consumption data and photovoltaic power generation data of an industrial park was used to establish an energy management model. The energy management system aimed to reduce operating costs and obtain optimal energy storage capacity, which is constrained by lithium battery performance and grid demand. With the operational cost and reasonable battery capacity as the optimization objectives, the Deep Deterministic Policy Gradient (DDPG) method, the greedy dynamic programming algorithm, and the genetic algorithm (GA) were adopted, where the performance of lithium battery and the requirement of power grid were the constraints. The simulation results show that compared with the current forms of energy, the three energy management methods reduced the cost of capacity and operating of the energy storage system by 18.9%, 36.1%, and 35.9%, respectively.

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Section: User Load Power Predictionmentioning

confidence: 99%

Machine Learning Based Optimization Model for Energy Management of Energy Storage System for Large Industrial Park

Gao

Hong

2021

Processes

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“…In recent years, the BDA group has primarily worked on developing methods and models that allow for more precise, faster or valuable utilization of vast amounts of heterogeneous and unreliable data. In doing so, the research group has explicitly focused on subjects like the development and combination of analytical methods with forecasting models (Blanc and Setzer 2016), the development of novel analytical approaches in the context of geographic IS (Wiener et al 2016) or the modelling and prediction of user behaviour based on heterogeneous field data (Schoch 2016).…”

Section: Business Data Analyticsmentioning

confidence: 99%

Information and Market Engineering at KIT: Quo Vadis?

et al. 2021

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Information systems (IS) are nowadays at the core of many personal and institutional activities and influence daily life more than ever before. To understand, evaluate and envision the forms of how we interact with IS, interdisciplinary and multifaceted research efforts are required. At the Information and Market Engineering chair at the Karlsruhe Institute of Technology, this task is taken head-on via research that stretches from user experiences to system design. In this review, the present research foci at the department are outlined, together with a brief description of its origins and the global developments that underly the necessity of conducting these particular IS studies.

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“…Based on these, they recommend using multiple levels of TA and combining the separate forecasts. This approach not only benefits from managing the modelling risk, but also utilises the established gains of forecast combination (Barrow and Kourentzes, 2016;Blanc and Setzer, 2016). Kourentzes et al (2014) provide empirical evidence to demonstrate gains over conventional forecasting.…”

Section: Multiple Temporal Aggregation Levelsmentioning

confidence: 99%