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The article deals with the operation of biogas stations, especially in terms of power supply to a 22kV distribution grid. The article includes a draft model the objective of which is to simulate power supply to the aforementioned 22kV grid. The main objective of the model is to verify the stability of power supply. Furthermore, this model will be used for automatic analysis of the data obtained through...
Agriculture is energy-intense, but also has a huge potential for generating energy directly at the point of use from renewables, e.g. biomass, photovoltaics and wind. In this paper, we investigate two models of collective arrangements among farmers in the federal state of Styria, Austria. These arrangements have the purpose to facilitate adoption of photovoltaics on farms. We contrast the two models...
Advances in service-oriented architectures (SOA), virtualization, high-speed networks, and cloud computing has resulted in attractive pay-as-you-go services. Job scheduling on these systems results in commodity bidding for computing time. This bidding is institutionalized by Amazon for its Elastic Cloud Computing (EC2) environment and bidding methods exist for other cloud-computing vendors as well...
Calibrated detailed energy models are often used to identify economic deep retrofit opportunities in existing buildings. But the uncertainty in building performance makes it technically unrealistic to reach a single “best fit” model without extensive sub-metering and domain experts on the project which is typically labor intensive. To address this problem, researchers have been adopting stochastic...
An interesting feature of some electricity logprice series is that they display daytime upward spikes and nighttime downward spikes. This paper will show that this phenomenology implies that the underlying data generating mechanism involves an implicit threshold behavior, and consequently that threshold stochastic dynamic models can be well suited to study this kind of series. As a worked out example,...
Transmission congestion occurs when power flow over a transmission line exceeds its thermal limit causes high fluctuation of locational marginal prices (LMPs). It eventually influences the revenue, incomes, and profits of the market participants. Hence an accurate LMP forecasting is important to ensure economical operation as well as proper scheduling of generation resources. It helps market participants...
Battery electric vehicles charged with renewable energies have the potential to reduce the greenhouse gas emissions of the transport sector substantially. To determine if electric vehicles cope with the needs of commuters, this article explains the modeling of user profiles based on statistical data. Furthermore, it evaluates energy expenses and mobility losses of the simulated commuter profiles....
In this paper, we propose an improved combined forecasting model integrates the merits of data pretreatment, combined model and Markov chain, known as Markov combined model. The moving average is used for the data pretreatment or determination of trend, combined model is designed for the trend forecasting, and the Markov chain is employed for modifying the forecasting results of combined model. The...
The use of ontologies for the interoperability of software models is widespread, with many applications also in the energy domain. By formulating a shared data structure and a definition of concepts and their properties, a language is created that can be used between modellers and-formalised in an ontology-between model components. When modelling energy systems, connections between different infrastructures...
Electricity is the basic industry of the national economy. The effective financial evaluation on thermoelectricity enterprises is an important mean and basic work to improve operation and circumvent risks. the financial evaluation of thermoelectricity project investment refers to an assessment process, which is the investment institution conducts a comprehensive analysis and evaluation on financial...
Medium-term electricity price forecasting is necessary for several applications in electricity markets, such as pricing derivatives, maintenance scheduling for generation companies, and budgeting and fuel contracting. However, this is a complex task because of the inherent dependence of price to other sometimes unpredictable variables, such as variations in availability of different supply resources...
The construction of Smart Grid will comprehensively enhance the intelligent level of every step in the power grid of our country. The data forecasting ability determines the quality of smart grid. As a result of this, this paper constructs the background value of grey GM (1, 1) model by using threepoint Gaussian quadrature formula, improves its accuracy and the data quality. It indicates that reconstruction...
Jiangsu province, the eastern coastal province with rapid economic development and high demand for electricity, power prediction seems very important. In this paper, we start from the current situation of electric power needs in Jiangsu province, a large number of historical data in Jiangsu province are taken as analytical basis, and gray model is used to forecast electricity consumption in Jiangsu...
This paper proposes a novel technique to forecast day-ahead electricity prices based on Panel Cointegration (PC). The current researches on the electricity price forecasting focus on the analysis of unstable economic time series. However, due to the difference of the allocation of power resource and consumption in different regions, the time series of electricity consumption and sales price in a single...
The ability of the liberalised energy markets to trigger investment in the generation capacity required to maintain an acceptable level of security of supply risk has been - and will continue to be - a topic of much debate. Modelling the dynamics of investment in generation capacity can inform this debate. More precisely, if investment is viewed as a negative feedback control mechanism with energy...
Any single one of the Auto-Regressive (AR) model, Moving Average (MA) model and Auto-Regressive and Moving Average (ARMA) model can not match the complex time-series data of electricity price, consequently the traditional Box-Jenkins method can not solve the forecasting of electricity price well. In this paper, fuzzy Box-Jenkins approach for modeling and short-term forecasting of the electricity price...
The paper proposed to use artificial neural networks (ANNs) to forecast electricity demand and to build the climate sensitive model. Digital simulation studies were conducted on the New Zealand electricity demand with temperature variations. The simulation study results have shown that the changes in temperature have a big impact on the expected total demand of electricity. As a result, it can be...
Aiming at the characters of day-ahead electricity price, an electricity price prediction model is established by combining the intelligent modeling methods. A new neural network method ELM is selected for its better performance to establish the basic day-ahead electricity price prediction model. Using the information fusion and ensemble ideas, a multiple ELM modeling approach is proposed to establish...
In the past decades, there has been an implicit explosion of absorption in Artificial Intelligence (AI) field. AI supplies robust and flexible means for acquiring solutions to a diversity of problems that often cannot be solved by other, more traditional and orthodox methods. Nowadays, its use is increasing rapidly in many sectors of complicated practical problems. Meanwhile, electrical energy demand...
Potential of thermoelectric power from bagasse by sugar mills of Pakistan was studied to explore whether a significant quantity of electricity could be produced from the biomass of this waste left as surplus after its use to fulfill mill's in-house requirements. The data concerning the production and disposal of bagasse and indigenous production of electricity for mill use by its incineration were...
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