The Supply and Demand Models Based on Electricity Consumption

2013 ◽  
pp. 79-105
Author(s):  
Zhaoguang Hu ◽  
Zheng Hu
Keyword(s):  
Supply And Demand ◽  
Electricity Consumption ◽  
Demand Models

scholarly journals Estimation of Supply and Demand Models for Chicken Meat in Turkey

2004 ◽  
Vol 25 (1) ◽  
pp. 45-48 ◽  
Author(s):  
Vedat Dagdemir ◽  
Okan Demir ◽  
Atilla Keskin
Keyword(s):  
Supply And Demand ◽  
Chicken Meat ◽  
Demand Models

scholarly journals The six-sphere framework: A practical tool for assessing monitoring and evaluation systems

2017 ◽  
Vol 5 (1) ◽  
Author(s):  
Kieron D. Crawley
Keyword(s):  
Evaluation System ◽  
Supply And Demand ◽  
Monitoring And Evaluation ◽  
Evaluation Systems ◽  
Key Stakeholders ◽  
Demand Models ◽  
Graphic Device ◽  
Practical Tool ◽  
Nuanced Understanding ◽  
Technical Capacity

Background: Successful evaluation capacity development (ECD) at regional, national and institutional levels has been built on a sound understanding of the opportunities and constraints in establishing and sustaining a monitoring and evaluation system. Diagnostics are one of the tools that ECD agents can use to better understand the nature of the ECD environment. Conventional diagnostics have typically focused on issues related to technical capacity and the ‘bridging of the gap’ between evaluation supply and demand. In so doing, they risk overlooking the more subtle organisational and environmental factors that lie outside the conventional diagnostic lens.Method: As a result of programming and dialogue carried out by the Centre for Learning on Evaluation and Results Anglophone Africa engaging with government planners, evaluators, civil society groups and voluntary organisations, the author has developed a modified diagnostic tool that extends the scope of conventional analysis.Results: This article outlines the six-sphere framework that can be used to extend the scope of such diagnostics to include considerations of the political environment, trust and collaboration between key stakeholders and the principles and values that underpin the whole system. The framework employs a graphic device that allows the capture and organisation of structural knowledge relating to the ECD environment.Conclusion: The article describes the framework in relation to other organisational development tools and gives some examples of how it can be used to make sense of the ECD environment. It highlights the potential of the framework to contribute to a more nuanced understanding of the ECD environment using a structured diagnostic approach and to move beyond conventional supply and demand models.

scholarly journals A Novel Pricing Algorithm Based on Reward-Punishment Mechanism for Supply and Demand Balancing

2021 ◽  
Vol 9 ◽  
Author(s):  
Xiaofei Chen ◽  
Liguo Weng ◽  
Haiyan Zhu ◽  
Deqiang Lian
Keyword(s):  
Electricity Market ◽  
Supply And Demand ◽  
Electricity Consumption ◽  
Electricity Demand ◽  
Single Type ◽  
Pricing Scheme ◽  
Punishment Mechanism ◽  
The Stability ◽  
Different Characteristics ◽  
Reward And Punishment

Demand response (DR) is a powerful tool to maintain the stability of the power system and maximize the profit of the electricity market, where the customers engage in the pricing scheme and adjust their electricity demand proactively based on the price. In DR programs, most existing works are based on the assumption that the prediction of the electricity demand from customers is always accurate and trustworthy, which will lead to high cost and fluctuation of the electricity market once the prediction is obeyed. In this paper, we design a reward and punishment mechanism to constrain customers’ dishonest behaviors and propose a novel pricing algorithm based on the reward and punishment mechanism to relax the assumption, which guarantees the total electricity demands of all customers are within a secure range and obtain the maximum profit of the supplier. Meanwhile, we obtain the optimal demand and provide a upper and lower bound of the proposed price for the electricity market. In addition to a single type of customer, we also consider multiple types of customers, each of whom has different characteristics to prices. Extensive simulation results are constructed to demonstrate the effectiveness of the proposed algorithm compared with other pricing algorithms. It also shows that the average electricity consumption of a whole community is mostly affected by the residents’ electricity consumption and the balance of the supply and all types of customers is achieved under the proposed pricing algorithm.

Experience Curves and Dynamic Demand Models: Implications for Optimal Pricing Strategies

1981 ◽  
Vol 45 (1) ◽  
pp. 52-62 ◽  
Author(s):  
Robert J. Dolan ◽  
Abel P. Jeuland
Keyword(s):  
Product Life Cycle ◽  
Dynamic Models ◽  
Supply And Demand ◽  
Pricing Strategy ◽  
Optimal Pricing ◽  
Pricing Strategies ◽  
Evolutionary Forces ◽  
Demand Models ◽  
Optimal Pricing Strategy ◽  
Experience Curves

Recent empirical research shows that supply and demand conditions are typically not stable over time. The evolution of these factors and the firm's ability to impact the evolution have important pricing implications. This paper presents a general methodology for determining the optimal pricing strategy over the product life cycle given evolutionary forces in the environment, and derives the optimal pricing strategy for some well known dynamic models.

Market supply and demand models

2013 ◽  
pp. 96-110
Author(s):  
Mik Wisniewski
Keyword(s):  
Supply And Demand ◽  
Demand Models

A Hybrid Decomposition Method for Integrating Coal Supply and Demand Models

1982 ◽  
Vol 30 (5) ◽  
pp. 887-906 ◽  
Author(s):  
Jeremy F. Shapiro ◽  
David E. White
Keyword(s):  
Decomposition Method ◽  
Supply And Demand ◽  
Demand Models ◽  
Coal Supply ◽  
Hybrid Decomposition

scholarly journals Accurate Deep Model for Electricity Consumption Forecasting Using Multi-channel and Multi-Scale Feature Fusion CNN–LSTM

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 1881 ◽  
Author(s):  
Xiaorui Shao ◽  
Chang-Soo Kim ◽  
Palash Sontakke
Keyword(s):  
Short Term Memory ◽  
Feature Fusion ◽  
Supply And Demand ◽  
Electricity Consumption ◽  
Sensor Data ◽  
Short Term ◽  
Multi Scale ◽  
Deep Model ◽  
Cost Of Time ◽  
The Impact

Electricity consumption forecasting is a vital task for smart grid building regarding the supply and demand of electric power. Many pieces of research focused on the factors of weather, holidays, and temperatures for electricity forecasting that requires to collect those data by using kinds of sensors, which raises the cost of time and resources. Besides, most of the existing methods only focused on one or two types of forecasts, which cannot satisfy the actual needs of decision-making. This paper proposes a novel hybrid deep model for multiple forecasts by combining Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) algorithm without additional sensor data, and also considers the corresponding statistics. Different from the conventional stacked CNN–LSTM, in the proposed hybrid model, CNN and LSTM extracted features in parallel, which can obtain more robust features with less loss of original information. Chiefly, CNN extracts multi-scale robust features by various filters at three levels and wide convolution technology. LSTM extracts the features which think about the impact of different time-steps. The features extracted by CNN and LSTM are combined with six statistical components as comprehensive features. Therefore, comprehensive features are the fusion of multi-scale, multi-domain (time and statistic domain) and robust due to the utilization of wide convolution technology. We validate the effectiveness of the proposed method on three natural subsets associated with electricity consumption. The comparative study shows the state-of-the-art performance of the proposed hybrid deep model with good robustness for very short-term, short-term, medium-term, and long-term electricity consumption forecasting.

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