scholarly journals Is Urbanisation Rate a Feasible Supplemental Parameter in Forecasting Electricity Consumption in China?

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Biao Yang ◽  
Yingcheng Li ◽  
Haokun Wei ◽  
Huan Lu

Traditional method of forecasting electricity consumption based only on GDP was sometimes ineffective. In this paper, urbanisation rate (UR) was introduced as an additional predictor to improve the electricity demand forecast in China at provincial scale, which was previously based only on GDP. Historical data of Shaanxi province from 2000 to 2013 was collected and used as case study. Four regression models were proposed and GDP, UR, and electricity consumption (EC) were used to establish the parameters in each model. The model with least average error of hypothetical forecast results in the latest three years was selected as the optimal forecast model. This optimal model divides total EC into four parts, of which forecasts can be made separately. It was found that GDP was only better correlated than UR on household EC, whilst UR was better on the three sectors of industries. It was concluded that UR is a valid predictor to forecast electricity demand at provincial level in China nowadays. Being provided the planned value of GDP and UR from the government, EC in 2015 were forecasted as 131.3 GWh.

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Ping Jiang ◽  
Qingping Zhou ◽  
Haiyan Jiang ◽  
Yao Dong

With rapid economic growth, electricity demand is clearly increasing. It is difficult to store electricity for future use; thus, the electricity demand forecast, especially the electricity consumption forecast, is crucial for planning and operating a power system. Due to various unstable factors, it is challenging to forecast electricity consumption. Therefore, it is necessary to establish new models for accurate forecasts. This study proposes a hybrid model, which includes data selection, an abnormality analysis, a feasibility test, and an optimized grey model to forecast electricity consumption. First, the original electricity consumption data are selected to construct different schemes (Scheme 1: short-term selection and Scheme 2: long-term selection); next, the iterative algorithm (IA) and cuckoo search algorithm (CS) are employed to select the best parameter of GM(1,1). The forecasted day is then divided into several smooth parts because the grey model is highly accurate in the smooth rise and drop phases; thus, the best scheme for each part is determined using the grey correlation coefficient. Finally, the experimental results indicate that the GM(1,1) optimized using CS has the highest forecasting accuracy compared with the GM(1,1) and the GM(1,1) optimized using the IA and the autoregressive integrated moving average (ARIMA) model.


Author(s):  
Thai Young Kim ◽  
Rommert Dekker ◽  
Christiaan Heij

Purpose The purpose of this paper is to show that intentional demand forecast bias can improve warehouse capacity planning and labour efficiency. It presents an empirical methodology to detect and implement forecast bias. Design/methodology/approach A forecast model integrates historical demand information and expert forecasts to support active bias management. A non-linear relationship between labour productivity and forecast bias is employed to optimise efficiency. The business analytic methods are illustrated by a case study in a consumer electronics warehouse, supplemented by a survey among 30 warehouses. Findings Results indicate that warehouse management systematically over-forecasts order sizes. The case study shows that optimal bias for picking and loading is 30-70 per cent with efficiency gains of 5-10 per cent, whereas the labour-intensive packing stage does not benefit from bias. The survey results confirm productivity effects of forecast bias. Research limitations/implications Warehouse managers can apply the methodology in their own situation if they systematically register demand forecasts, actual order sizes and labour productivity per warehouse stage. Application is illustrated for a single warehouse, and studies for alternative product categories and labour processes are of interest. Practical implications Intentional forecast bias can lead to smoother workflows in warehouses and thus result in higher labour efficiency. Required data include historical data on demand forecasts, order sizes and labour productivity. Implementation depends on labour hiring strategies and cost structures. Originality/value Operational data support evidence-based warehouse labour management. The case study validates earlier conceptual studies based on artificial data.


Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5332 ◽  
Author(s):  
Marcin Malec ◽  
Grzegorz Kinelski ◽  
Marzena Czarnecka

The COVID-19 pandemic has caused changes in electricity demand and, consequently, electricity consumption profiles. Given the rapid changes in energy prices, it is significant from the perspective of energy companies, and forecasting consumed energy volume. A necessity for accurate energy volume planning forces the need for analyzing consumers’ behaviors during the pandemic, especially under lockdowns, to prepare for the possibility of another pandemic wave. Many business clients analyzed in the paper are economic entities functioning in sectors under restrictions. That is why analyzing the pandemic’s impact on the change in energy consumption profiles and volume of these entities is particularly meaningful. The article analyzes the pandemic and restrictions’ impact on the total change of energy consumption volume and demand profiles. The analysis was conducted basing on data collected from a Polish energy trading and sales company. It focused on the energy consumption of its corporate clients. Analyzed data included aggregated energy consumption volumes for all company’s customers and key groups of economic entities under restrictions. The analysis demonstrates the influence of pandemic restrictions on energy consumption in the group of business clients. Significant differences are observable among various sectors of the economy. The research proves that the largest drops in energy consumption are related to shopping centers and offices. Altogether, the restrictions have caused a 15–23% energy consumption drop during the first lockdown and a maximum 11% during the second against expected values.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Timothy King Avordeh ◽  
Samuel Gyamfi ◽  
Alex Akwasi Opoku

Purpose The purpose of this paper is to investigate the impact of temperature on residential electricity demand in the city of Greater Accra, Ghana. It is believed that the increasing trend of temperatures may significantly affect people’s lives and demand for electricity from the national grid. Given the recurrent electricity crisis in Ghana, this study will investigate both the current and future residential energy demands in the light of temperature fluctuations. This will inform future power generation using renewable energy resources mix to find a sustainable solution to the recurrent energy demand challenges in Ghana. This study will help the Government of Ghana to better understand the temperature dependence of residential energy demand, which in turn will help in developing behavioral modification programs aimed at reducing energy consumption. Monthly data for the temperature and residential electricity consumption for Greater Accra Region from January 2007 to December 2018 obtained from the Ghana Meteorological Service (GMS) and Ghana Grid Company (Gridco), respectively, are used for the analysis. Design/methodology/approach This study used monthly time series data from 2007 to 2018. Data on monthly electricity demand and temperature are obtained from the Ghana Grid Company and GMS. The theoretical framework for residential electricity consumption, the log-linear demand equation and time series regression approaches was used for this study. To demonstrate certain desirable properties and to produce good estimators in this study, an analysis technique of ordinary least squares measurement was also applied. Findings This study showed an impact on residential electricity requirements in the selected regions of Greater Accra owing to temperature change. The analysis suggests a substantial positive response to an increase in temperature demand for residential electricity and thus indicates a growth of the region’s demand for electricity in the future because of temperature changes. As this analysis projects, the growth in the electricity demand seems too small for concern, perhaps because of the incoherence of the mechanisms used to regulate the temperature by the residents. However, two points should be considered when drawing any conclusions even in the case of Greater Accra alone. First, the growth in the demand for electricity shown in the present study is the growth of demand due only to increasing temperatures that do not consider changes in all the other factors driving the growth of demand. The electricity demand will in the future increase beyond what is induced by temperature, due to increasing demand, population and mechanization and other socioeconomic factors. Second, power consumption understated genuine electricity demand, owing to the massive shedding of loads (Dumsor) which occurred in Ghana from 2012 to 2015 in the analysis period that also applies in the Greater Accra region. Given both of these factors, the growth in demand for electricity is set to increase in response to climate change, which draws on the authorities to prepare more critically on capacity building which loads balancing. The results also revealed that monthly total residential electricity consumption, particularly the monthly peak electricity consumption in the city of Accra is highly sensitive to temperature. Therefore, the rise in temperature under different climate change scenarios would have a high impact on residential electricity consumption. This study reveals that the monthly total residential electricity demand in Greater Accra will increase by up to 3.1%. Research limitations/implications The research data was largely restricted to only one region in Ghana because of the inconsistencies in the data from the other regions. The only climate variable use was temperature because it was proven in the literature that it was the most dominant variable that affects electricity demand, so it was not out of place to use only this variable. The research, however, can be extended to capture the entire regions of the country if sponsorship and accurate data can be obtained. Practical implications The government as the policy and law-making authority has to play the most influential role to ensure adaptation at all levels toward the impact of climate change for residential consumers. It is the main responsibility of the government to arrange enough supports to help residential consumers adapt to climate change and try to make consumers self-sufficient by modification of certain behaviors rather than supply dependent. Government bodies need to carefully define their climate adaptation supports and incentive programs to influence residential-level consumption practices and demand management. Here, energy policies and investments need to be more strategic. The most critical problem is to identify the appropriate adaptation policies that favor the most vulnerable sectors such as the residential sector. Social implications To evaluate both mitigation and adaptation policies, it is important to estimate the effect of climate change on energy usage around the world. Existing empirical figures, however, are concentrated in Western nations, especially the USA. To predict how electricity usage will shift in the city of Greater Accra, Ghana, the authors used regular household electricity consumption data. Originality/value The motivation for this paper and in particular the empirical analysis for Ghana is originality for the literature. This paper demonstrates an adequate understanding of the relevant literature in modern times.


FEDS Notes ◽  
2020 ◽  
Vol 2020 (2792) ◽  
Author(s):  
Joshua Blonz ◽  
◽  
Jacob Williams ◽  

Electricity is used by all businesses in the United States. During quickly moving economic shocks—for example, a pandemic or natural disaster—changes in electricity consumption can provide insight to policymakers before traditional survey-based metrics, which can lag weeks or months behind economic conditions and typically only show a snapshot of when the survey was conducted.


2019 ◽  
Vol 11 (13) ◽  
pp. 3656
Author(s):  
Oscar Trull ◽  
Angel Peiró-Signes ◽  
J. Carlos García-Díaz

The forecast of electricity consumption plays a fundamental role in the environmental impact of a tourist destination. Poor forecasting, under certain circumstances, can lead to huge economic losses and air pollution, as prediction errors usually have a large impact on the utilisation of fossil fuel-generation plants. Due to the seasonality of tourism, consumption in areas where the industry represents a big part of the economic activity follows a different pattern than in areas with a more regular economic distribution. The high economic impact and seasonality of the tourist activity suggests the use of variables specific to it to improve the electricity demand forecast. This article presents a Holt–Winters model with a tourism indicator to improve the effectiveness on the electricity demand forecast in the Balearic Islands (Spain). Results indicate that the presented model improves the accuracy of the prediction by 0.3%. We recommend the use of this type of model and indicator in tourist destinations where tourism accounts for a substantial amount of the Gross Domestic Product (GDP), we can control a significant amount of the flow of tourists and the electrical balance is controlled mainly by fossil fuel power plants.


2021 ◽  
Vol 11 (10) ◽  
pp. 4506
Author(s):  
Yazao Yang ◽  
Avishai (Avi) Ceder ◽  
Weiyong Zhang ◽  
Haodong Tang

The unconstrained demand forecast for car rentals has become a difficult problem for revenue management due to the need to cope with a variety of rental vehicles, the strong subjective desires and requests of customers, and the high probability of upgrading and downgrading circumstances. The unconstrained demand forecast mainly includes repairing of constrained historical demand and forecasting of future demand. In this work, a new methodology is developed based on multiple discrete choice models to obtain customer choice preference probabilities and improve a known spill model, including a repair process of the unconstrained demand. In addition, the linear Holt–Winters model and the nonlinear backpropagation neural network are combined to predict future demand and avoid excessive errors caused by a single method. In a case study, we take advantage of a stated preference and a revealed preference survey and use the variable precision rough set to obtain factors and weights that affect customer choices. In this case study and based on a numerical example, three forecasting methods are compared to determine the car rental demand of the next time cycle. The comparison with real demand verifies the feasibility and effectiveness of the hybrid forecasting model with a resulting average error of only 3.06%.


2021 ◽  
Vol 293 ◽  
pp. 02063
Author(s):  
Anrui Li ◽  
Shi Su ◽  
Tong Han ◽  
Chunlin Yin ◽  
Jie Li ◽  
...  

Energy demand forecast has an important practical significance for the sustainable development of the national economy, the reasonable allocation of resources, and the construction of modernization goals. This study is based on the analysis of coal, electricity, natural gas, and other energy data in Yunnan Province from 2011 to 2018 and uses long short-term memory, sequence to sequence, deep learning, and ridge regression coupling methods to construct an energy demand forecast model in Yunnan Province. Forecast results show the following. The total energy consumption of Yunnan Province from 2021 to 2025 will continue to increase. Moreover, the coal consumption of Yunnan Province will continue to decline from 2021 to 2025. Furthermore, the electricity consumption of Yunnan will increase by about 8.02% year-on-year from 2021 to 2025. The experiment proves that the forecasting effect of the energy demand forecast model proposed in this study is excellent.


2018 ◽  
Vol 49 ◽  
pp. 02007 ◽  
Author(s):  
Jaka Windarta ◽  
Bambang Purwanggono ◽  
Fuad Hidayanto

Electricity demand forecasting is an important part in energy management especially in electricity planning. Indonesia is a large country with a pattern of electricity consumption which continues to increase, therefor need to forecasting electricity demand in order to avoid unbalance demand and supply or deficit energy. LEAP (Long-range Energy Alternative Planning System) as a tool energy model and Indonesia as a case study. Basically, electricity demand is influenced by population, economy and electricity intensity. The purpose of this study is to provide understanding and application of electricity demand forecasting by using LEAP. The base year is 2010 and end year projection is 2025. The scenarios of simulated model consist of two scenarios. They are Business as Usual (BAU) and Government policy scenario. Results of both scenarios indicate that end year electricity demand forecasting in Indonesia increased more than two fold compared to base year.


Processes ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 320 ◽  
Author(s):  
Yuan-Jia Ma ◽  
Ming-Yue Zhai

Improved-performance day-ahead electricity demand forecast is important to deliver necessary information for right decision of energy management of microgrids. It supports microgrid operators and stakeholders to have better decisions on microgrid flexibility, stability and control. The available conventional forecasting methods for electricity demand at national or regional level are not effective for electricity demand forecasting in microgrids. This is due to the fact that the electricity consumption in microgrids is many times less than the regional or national demands and it is highly volatile. In this paper, an integrated Artificial Intelligence (AI) based approach consisting of Wavelet Transform (WT), Simulated Annealing (SA) and Feedforward Artificial Neural Network (FFANN) is devised for day-ahead prediction of electric power consumption in microgrids. The FFANN is the basic forecasting engine of the proposed model. The WT is utilized to extract relevant features of the target variable (electric load data series) to obtain a cluster of enhanced-feature subseries. The extracted subseries of the past values of the electric load demand data are employed as the target variables to model the FFANN. The SA optimization technique is employed to obtain the optimal values of the FFANN weight parameters during the training process. Historical information of actual electricity consumption, meteorological variables, daily variations, weekly variations, and working/non-working day indicators have been employed to develop the forecasting tool of the devised integrated AI based approach. The approach is validated using electricity demand data of an operational microgrid in Beijing, China. The prediction results are presented for future testing days with one-hour time interval. The validation results demonstrated that the devised approach is capable to forecast the microgrid electricity demand with acceptably small error and reasonably short computation time. Moreover, the prediction performance of the devised approach has been evaluated relative to other four approaches and resulted in better prediction accuracy.


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