Dynamic transverse correction method of middle and long term energy forecasting based on statistic of forecasting errors

Xue, Bike; Geng, Junping

doi:10.1109/asscc.2012.6523273

Cited by 9 publications

(7 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to identify the correlation between this behavior and electricity demand, we can use several models based on time series. Some authors [36] classify these models according to the estimation period: short, medium, and long term.…”

Section: Resultsmentioning

confidence: 99%

Solar Energy Production for a Decarbonization Scenario in Spain

2019

View full text Add to dashboard Cite

Solar energy is one of the most promising sources of energy that could be used to address distributed supply problems. Global warming and decarbonization are significant global concerns, particularly for countries that are not fossil fuel providers. This paper presents a study focused on Spain, a country with a favorable location with respect to horizontal irradiance. The study addresses the future energy demand forecast and how photovoltaic energy could supply an important part of electricity needs. Our approach focuses on two analyses. First, several traditional statistical techniques are discussed in order to obtain a model that best suits Spanish energy demand forecasts for the future years. Different algorithms are compared in order to determine which is the most appropriate for the considered purpose. Second, the evolution of solar photovoltaic technology in Spain is analyzed. The latitude of Spanish cities makes them suitable for utilizing this kind of technology. In this sense, seasonal and monthly trends are identified with high levels of detail, considering a large historical dataset. The increase of the capacity of electricity generation based on this procedure is evaluated. Finally, a discussion about matching electricity demand forecasts and photovoltaic production is offered. Considering the selected model for the photovoltaic power of Spain, from 5 to 42 GW in 2030, the Spanish production is determined as 81 TWh. The obtained results suggest that a possible energy transition is feasible. However, some challenges have to be considered, such us the design of an effective strategy to store excess energy produced when generation is higher than electricity demand. In this way, the electrical distribution system could be fed by the stored energy when solar energy production is deficient.

show abstract

Section: Resultsmentioning

confidence: 99%

Solar Energy Production for a Decarbonization Scenario in Spain

2019

View full text Add to dashboard Cite

show abstract

“…According to different authors (e.g., [ 17 , 24 ]), demand forecasting can be classified into three categories with respect to the forecasting horizon: Short-term (typically one hour to one week). Medium-term (typically one week to one year).…”

Section: Important Factors In Demand Forecastingmentioning

confidence: 99%

An Insight of Deep Learning Based Demand Forecasting in Smart Grids

Aguiar-Pérez

Pérez-Juárez

2023

Sensors

View full text Add to dashboard Cite

Smart grids are able to forecast customers’ consumption patterns, i.e., their energy demand, and consequently electricity can be transmitted after taking into account the expected demand. To face today’s demand forecasting challenges, where the data generated by smart grids is huge, modern data-driven techniques need to be used. In this scenario, Deep Learning models are a good alternative to learn patterns from customer data and then forecast demand for different forecasting horizons. Among the commonly used Artificial Neural Networks, Long Short-Term Memory networks—based on Recurrent Neural Networks—are playing a prominent role. This paper provides an insight into the importance of the demand forecasting issue, and other related factors, in the context of smart grids, and collects some experiences of the use of Deep Learning techniques, for demand forecasting purposes. To have an efficient power system, a balance between supply and demand is necessary. Therefore, industry stakeholders and researchers should make a special effort in load forecasting, especially in the short term, which is critical for demand response.

show abstract

“…To predict future values of a time-dependent magnitude (activity, structure, or intensity for a certain sector), time-series forecasting models were employed. Some authors [54] have classified these models according to the estimation period: short-, medium-, or long-term.…”

Section: Time-series Forecastingmentioning

confidence: 99%

Long-Term Demand Forecasting in a Scenario of Energy Transition

2019

View full text Add to dashboard Cite

The energy transition from fossil fuels to carbon-free sources will be a big challenge in the coming decades. In this context, the long-term prediction of energy demand plays a key role in planning energy infrastructures and in adopting economic and energy policies. In this article, we aimed to forecast energy demand for Spain, mainly employing econometrics techniques. From information obtained from institutional databases, energy demand was decomposed into many factors and economy-related activity sectors, obtaining a set of disaggregated sequences of time-dependent values. Using time-series techniques, a long-term prediction was then obtained for each component. Finally, every element was aggregated to obtain the final long-term energy demand forecast. For the year 2030, an energy demand equivalent to 82 million tons of oil was forecast. Due to improvements in energy efficiency in the post-crisis period, a decoupling of economy and energy demand was obtained, with a 30% decrease in energy intensity for the period 2005–2030. World future scenarios show a significant increase in energy demand due to human development of less developed economies. For Spain, our research concluded that energy demand will remain stable in the next decade, despite the foreseen 2% annual growth of the nation’s economy. Despite the enormous energy concentration and density of fossil fuels, it will not be affordable to use them to supply energy demand in the future. The consolidation of renewable energies and increasing energy efficiency is the only way to satisfy the planet’s energy needs.

show abstract

Dynamic transverse correction method of middle and long term energy forecasting based on statistic of forecasting errors

Cited by 9 publications

References 0 publications

Solar Energy Production for a Decarbonization Scenario in Spain

Solar Energy Production for a Decarbonization Scenario in Spain

An Insight of Deep Learning Based Demand Forecasting in Smart Grids

Long-Term Demand Forecasting in a Scenario of Energy Transition

Contact Info

Product

Resources

About