• Energy Research
• 2021-2025close

Reducing the usage of fossil fuels is a central issue in ongoing policy debates. This in particular holds for Dutch horticulture, given its energy-intensive production. We analyse differences in energy usage and price responsiveness of horticultural firms by estimating energy demand functions using a Bayesian random coefficient model. Beyond, the effects of a proposed energy tax are assessed. Allowing for firm-specific energy price coefficients gives a better model fit compared to conventional models with fixed slope parameters. This confirms that firms respond differently to energy prices, which is taken into account in simulating the effects of more restrictive energy policies. The results show that larger-sized firms use less gas per square meter yet also point at a considerable spread in additional energy expenses between firms.

Energy demand modelling is essential for reliable informing and framing energy policy decisions. More accurate modelling betters ensuring availability of energy and energy quality. Energy availability is related to energy access across the country and defines important economic measures such as energy poverty, which plays a critical role in developing countries. Energy quality is related to the reliability of the supply for correctly estimating energy needs. To incorporate spatial and temporal components of energy in a way that availability and quality are accurately assessed, this article discussed a number of suitable task methods for this (Second-generation GAMs with one-dimensional smoothers: Cyclic/Non-Cyclic Cubic Splines and two-dimensional smoothers: Markov Random Fields/Tensor Splines Interactions). The results showed that the complete consideration of both temporal and spatial aspects leads to a better fitted model which explains more of the data variation.

AbstractReducing the usage of fossil fuels is a central issue in ongoing policy debates. This in particular holds for Dutch horticulture, given its energy-intensive production. We analyse differences in energy usage and price responsiveness of horticultural firms by estimating energy demand functions using a Bayesian random coefficient model. Beyond, the effects of a proposed energy tax are assessed. Allowing for firm-specific energy price coefficients gives a better model fit compared to conventional models with fixed slope parameters. This confirms that firms respond differently to energy prices, which is taken into account in simulating the effects of more restrictive energy policies. The results show that larger-sized firms use less gas per square meter yet also point at a considerable spread in additional energy expenses between firms.

In this study, discrete choice models that combine different behavioural rules are estimated to study the visitors’ preferences in relation to their travel mode choices to access a national park. Using a revealed preference survey conducted on visitors of Teide National Park (Tenerife, Spain), we present a hybrid model specification—with random parameters—in which we assume that some attributes are evaluated by the individuals under conventional random utility maximization (RUM) rules, whereas others are evaluated under random regret minimization (RRM) rules. We then compare the results obtained using exclusively a conventional RUM approach to those obtained using both RUM and RRM approaches, derive monetary valuations of the different components of travel time and calculate direct elasticity measures. Our results provide useful instruments to evaluate policies that promote the use of more sustainable modes of transport in natural sites. Such policies should be considered as priorities in many national parks, where negative transport externalities such as traffic congestion, pollution, noise and accidents are causing problems that jeopardize not only the sustainability of the sites, but also the quality of the visit.

Taking the invention patents of the C9 League from 2002 to 2020 as samples, a random survival forest model is established to predict the dynamic time-point of patent transfer cycle. By ranking the variables based on importance, it is found that the countries citing, the non-patent citations and the backward citations have significant impacts on the patent transfer cycle. C-index, Brier score and integrated Brier score are used to measure the discrimination and calibration ability of the four different survival models respectively. It is found that the prediction accuracy of the random survival forest model is higher than that of the Cox proportional risk model, Cox model based on lasso penalty and random forest model. In addition, the survival function and cumulative risk function under the random survival forest are adopted to predict and analyze the individual university patent transfer cycle, which shows that the random survival forest model has good prediction performance and is able to help universities as well as enterprises to identify the patent transfer opportunities effectively, thereby shortening the patent transfer cycle and improving the patent transfer efficiency.

This paper addresses the problem of overloading in power distribution networks, which stems from the transmission systems being incapable of delivering power from source to consumers during peak hours. This causes frequent power-outages (or blackouts), requiring the consumers to rely on alternative energy sources, e.g., Uninterrupted Power Supply (UPS) systems with batteries to meet their essential needs. This paper proposes a demand response (DR) framework to eliminate the problem of network overloading. The flexibility in the consumption of batteries and air conditioners (ACs) is exploited in the proposed framework. The operation of ACs is manipulated while maintaining occupant comfort, and the power flow from mains and batteries is scheduled based on an ensemble of demand forecast avoiding network overloading and consequent power-outages. The problem is modeled in an optimal control setting and solved using a stochastic model predictive control strategy, and a computationally effective method is also proposed to efficiently solve the underlying optimization problems. Towards the end, simulation results show the efficacy of the proposed framework to avoid overloading.

The building sector in Sweden is responsible for 40 % of the final energy use, and space heating largely contributes to the total energy demand. Minimum energy performance standards and building codes are becoming more strict and comprehensive globally. In the European Union there are substantial goals of achieving a higher energy efficiency in the residential building sector. Energy Performance Certificates (EPCs) are a tool that can be used to track and assess building energy in a standardized way, to follow up on the energy efficiency of buildings. Privately owned single-family houses do not need to have a valid EPC, it is only mandatory to perform an assessment when the house is subject to sale, meaning that only about 20 % of single-family houses in Sweden have an EPC. The objective of the study is therefore to develop a data driven model based on existing EPC data, to estimate the annual space heating energy demand of Swedish single family houses that do not have an EPC. Sub-objectives include testing different data-driven machine learning models to identify which algorithm achieves the highest accuracy. Input features such as floor area, construction year, geographical location and heating source were used to make predictions. The results show that out of the investigated models, the Histogram-GBM performed the best when predicting the space heating energy of an individual house, with a RMSE of 24.33 kWh/m2 /year, MAE of 17.50 kWh/m2 /year, MAPE of 24.62 % and R2 -value of 0.736. When evaluating the results of predictions for the entire single-family housing stock, the errors of each individual house are hidden within the entire housing stock, minimizing the error of the total prediction. The best performing model, Histogram GBM, achieved a MBE of 0.59 kWh/prediction, equivalent to an error smaller than 0.005 %. I Sverige står byggnadssektorn för 40 % av den slutliga energianvändningen, och uppvärmning av byggnader är en stor andel av den totala energianvändningen. Minimikrav för energiprestanda och byggregler blir globalt allt mer strikta och omfattande. EU har betydande mål att uppnå högre energieffektivitet för alla typer av bostadshus. Energideklarationer (EPC) är ett verktyg som kan användas för att samla in och bedömma energianvändning i byggnader på ett standardiserat sätt, för att kunna följa upp energieffektivisering av byggnader. Till skillnad från andra byggnadstyper behöver privat ägda småhus inte ha en giltig energideklaration, de är endast obligatoriska att utfärda när huset säljs, vilket betyder att bara 20 % av småhus i Sverige har en energideklaration. Syftet med studien är därför att utveckla en datadriven modell baserat på data från existerande energideklarationer, för att estimera den årliga värmeenergianvändningen i svenska småhus som saknar en energideklaration. Syftet är också att testa olika data-drivna maskininlärningsmodeller för att identifiera vilken som uppnår det lägsta felet. I modellerna har indata såsom golvyta, byggår, geografisk plats och värmekälla använts för att göra estimeringar. Resultaten visar att av de undersökta modellerna var Histogram-GBM den bästa för att estimera värmeanvändning, med ett RMSE på 24.33 kWh/m2 /år, MAE på 17.50 kWh/m2 /år, MAPE på 24.62 % och ett R2 -värde på 0.736. Resultaten visar också att när värmeanvändning estimeras för hela beståndet av småhus, minimeras det totala felet när alla kvarvarande fel summeras. Den bästa modellen, Histogram-GBM uppnådde ett MBE på 0.59 kWh/estimering, vilket motsvarar ett fel mindre än 0.005 %.

Fossil fuel consumption has triggered worries about energy security and climate change; this has promoted hydrogen as a viable option to aid in decarbonizing global energy systems. Hydrogen could substitute for fossil fuels in the future due to the economic, political, and environmental concerns related to energy production using fossil fuels. However, currently, the majority of hydrogen is produced using fossil fuels, particularly natural gas, which is not a renewable source of energy. It is therefore crucial to increase the efforts to produce hydrogen from renewable sources, rather from the existing fossil-based approaches. Thus, this study investigates how renewable energy can accelerate the production of hydrogen fuel in the future under three hydrogen economy-related energy regimes, including nuclear restrictions, hydrogen, and city gas blending, and in the scenarios which consider the geographic distribution of carbon reduction targets. A random effects regression model has been utilized, employing panel data from a global energy system which optimizes for cost and carbon targets. The results of this study demonstrate that an increase in renewable energy sources has the potential to significantly accelerate the growth of future hydrogen production under all the considered policy regimes. The policy implications of this paper suggest that promoting renewable energy investments in line with a fairer allocation of carbon reduction efforts will help to ensure a future hydrogen economy which engenders a sustainable, low carbon society.