Assessing the potential and costs of reducing energy demand

Llamas, Pedro Linares; Pintos, Pablo; Würzburg, Klaas

doi:10.1007/s41825-017-0004-5

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…In (1)(2)(3)(4)(5), the variables represent the following meanings: = Forget gate, = Input gate, = Output gate, ′ = Intermediate cell state, and = Cell state. These are the equations of gates and cell states of the LSTM.…”

Section: Casual Structure Of Proposed Rf-bi-lstm Hybrid Modelmentioning

confidence: 99%

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A short-term hybrid forecasting model for time series electrical-load data using random forest and bidirectional long short-term memory

Ferdoush

Mahmud

Chakrabarty

et al. 2021

IJECE

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In the presence of the deregulated electric industry, load forecasting is more demanded than ever to ensure the execution of applications such as energy generation, pricing decisions, resource procurement, and infrastructure development. This paper presents a hybrid machine learning model for short-term load forecasting (STLF) by applying random forest and bidirectional long short-term memory to acquire the benefits of both methods. In the experimental evaluation, we used a Bangladeshi electricity consumption dataset of 36 months. The paper provides a comparative study between the proposed hybrid model and state-of-art models using performance metrics, loss analysis, and prediction plotting. Empirical results demonstrate that the hybrid model shows better performance than the standard long short-term memory and the bidirectional long short-term memory models by exhibiting more accurate forecast results.

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Section: Casual Structure Of Proposed Rf-bi-lstm Hybrid Modelmentioning

confidence: 99%

“…In deregulated electricity markets, energy transactions and price of electricity are critically conditional on forecast electric loads. The operational cost may rise remarkably as an outcome of underestimating the energy demand [3].…”

Section: Introductionmentioning

confidence: 99%

A short-term hybrid forecasting model for time series electrical-load data using random forest and bidirectional long short-term memory

Ferdoush

Mahmud

Chakrabarty

et al. 2021

IJECE

View full text Add to dashboard Cite

show abstract

“…They found that, even though the automatic light control was installed in the office, the occupant preferred to keep the blinds closed to prevent the automatic control system from switching off lights when there was adequate daylight. Besides, the cost of technologyrelated ECMs grows increasingly higher when no-and low-cost measures run out, and developers have to implement high-cost measures [7]. Due to these limitations of technological innovations and improvements, it is critical to adopt behaviour-related ECMs in order to yield further energy savings.…”

Section: Introductionmentioning

confidence: 99%

A framework for estimating the energy-saving potential of occupant behaviour improvement

Hong

Chou

2021

Applied Energy

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Energy-related occupant behaviour in buildings has demonstrated considerable energysaving potential. However, the current modelling method of occupant behaviour does not give sufficient considerations on the implementation difficulty of behaviour and provide a holistic map from survey data to various behaviour models.This article proposes a holistic survey-and-simulation-based framework for estimating the energysaving potential of occupant behaviour improvement. In the framework, seven typical categories of occupant behaviour models are identified based on the survey results. According to the implementation difficulty, the models are integrated into four behaviour styles (baseline, wasteful, moderate and austere) to represent different levels of energy-saving consciousness of occupants. Based on a case study with a nationwide survey in Singapore, there are remarkable energy savings potential if occupant behaviour is improved; the building energy consumption can be reduced by up to 9.5% with the moderate behaviour improvement, and up to 21.0% with the aggressive behaviour improvement. The simulation results accord well with the measured results within a reasonable range of deviation. The framework can be applied to estimate the energy-saving potential of occupant behaviour improvement in a building with affordable cost, and the findings can inform a behaviour improvement program with effective and efficient measures.

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“…Cogeneration and polygeneration systems are an essential part of industrial production of materials and energies, consuming or generating heat, electricity, mechanical, and chemical energy [1,2]. Ambitious efforts of national and European institutions to reduce greenhouse gases emissions and to ensure sustainable industrial production [3][4][5][6][7][8][9] at the same time cannot be successfully met without increasing material and energy efficiency of the industry [10][11][12][13] by simultaneous energy, economic, environmental, and risk and safety optimization [14][15][16] of existing industrial cogeneration and polygeneration systems [17,18].…”

Section: Introductionmentioning

confidence: 99%

Process Drive Sizing Methodology and Multi-Level Modeling Linking MATLAB® and Aspen Plus® Environment

et al. 2020

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Optimal steam process drive sizing is crucial for efficient and sustainable operation of energy-intense industries. Recent years have brought several methods assessing this problem, which differ in complexity and user-friendliness. In this paper, a novel complex method was developed and presented and its superiority over other approaches was documented on an industrial case study. Both the process-side and steam-side characteristics were analyzed to obtain correct model input data: Driven equipment performance and efficiency maps were considered, off-design and seasonal operation was studied, and steam network topology was included. Operational data processing and sizing calculations were performed in a linked MATLAB®–Aspen Plus® environment, exploiting the strong sides of both software tools. The case study aimed to replace a condensing steam turbine by a backpressure one, revealing that: 1. Simpler methods neglecting frictional pressure losses and off-design turbine operation efficiency loss undersized the drive and led to unacceptable loss of deliverable power to the process; 2. the associated process production loss amounted up to 20%; 3. existing bottlenecks in refinery steam pipelines operation were removed; however, new ones were created; and 4. the effect on the marginal steam source operation may vary seasonally. These findings accentuate the value and viability of the presented method.

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Assessing the potential and costs of reducing energy demand

Cited by 8 publications

References 13 publications

A short-term hybrid forecasting model for time series electrical-load data using random forest and bidirectional long short-term memory

A short-term hybrid forecasting model for time series electrical-load data using random forest and bidirectional long short-term memory

A framework for estimating the energy-saving potential of occupant behaviour improvement

Process Drive Sizing Methodology and Multi-Level Modeling Linking MATLAB® and Aspen Plus® Environment

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