Trend extrapolation in long-term forecasting: An investigation using New Zealand electricity consumption data

1986 ◽  
Vol 30 (2) ◽  
pp. 167-188 ◽  
Author(s):  
P.S. Bodger ◽  
Tay H.S.
Keyword(s):  
New Zealand ◽  
Electricity Consumption ◽  
Consumption Data ◽  
Long Term Forecasting

scholarly journals Econometric Model for Forecasting Electricity Demand of Industry and Construction Sectors in Vietnam to 2030

2020 ◽  
Vol 3 (1) ◽  
pp. First
Author(s):  
Viết Cường Võ ◽  
Phuong Hoang Nguyen ◽  
Luan Le Duy Nguyen ◽  
Van-Hung Pham
Keyword(s):  
Power System ◽  
Econometric Model ◽  
Electricity Consumption ◽  
Electricity Demand ◽  
Master Plan ◽  
Base Scenario ◽  
Leading Position ◽  
Long Term Forecasting ◽  
High Scenario

An accurate forecasting for long-term electricity demand makes a major role in the planning of the power system in any country. Vietnam is one of the most economically developing countries in the world, and its electricity demand has been increased dramatically high of about 15%/y for the last three decades. Contribution of industry and construction sectors in GDP has been increasing year by year, and are currently holding the leading position of largest consumers with more than 50% sharing in national electricity consumption proportion. How to estimate the electricity consumption of these sectors correctly makes a crucial contribution to the planning of the power system. This paper applies an econometric model with Cobb Douglas production function - a top-down method to forecast electricity demand of the industry and construction sectors in Vietnam to 2030. Four variables used are the value of the sectors in GDP, income per person, the proportion of electricity consumption of the sectors in total, and electric price. Forecasted results show that the proposed method has a quite low MAPE of 7.66% for long-term forecasting. Variable of electric price does not affect the demand. This is a very critical result of the study for authority governors in Vietnam. In the base scenario of the GDP and the income per person, the forecasted electricity demands of the sectors are 112,853 GWh, 172,691 GWh, and 242,027 GWh in 2020, 2025, 2030, respectively. In high scenario one, the demands are 115,947 GWh, 181,591 GWh, and 257,272 GWh, respectively. The above values in the high scenario are less than from 9.0% to 15.8 % of that of the based on in the Revised version of master plan N0. VII.

scholarly journals A Data-Driven Clustering Analysis for the Impact of COVID-19 on the Electricity Consumption Pattern of Zhejiang Province, China

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 8187
Author(s):  
Zhiang Zhang ◽  
Ali Cheshmehzangi ◽  
Saeid Pourroostaei Ardakani
Keyword(s):  
Electricity Consumption ◽  
Zhejiang Province ◽  
Data Driven ◽  
Time Shift ◽  
Consumption Pattern ◽  
Short Term ◽  
Consumption Data ◽  
Multiple Regions ◽  
The Impact

The COVID-19 pandemic has impacted electricity consumption patterns and such an impact cannot be analyzed by simple data analytics. In China, specifically, city lock-down policies lasted for only a few weeks and the spread of COVID-19 was quickly under control. This has made it challenging to analyze the hidden impact of COVID-19 on electricity consumption. This paper targets the electricity consumption of a group of regions in China and proposes a new clustering-based method to quantitatively investigate the impact of COVID-19 on the industrial-driven electricity consumption pattern. This method performs K-means clustering on time-series electricity consumption data of multiple regions and uses quantitative metrics, including clustering evaluation metrics and dynamic time warping, to quantify the impact and pattern changes. The proposed method is applied to the two-year daily electricity consumption data of 87 regions of Zhejiang province, China, and quantitively confirms COVID-19 has changed the electricity consumption pattern of Zhejiang in both the short-term and long-term. The time evolution of the pattern change is also revealed by the method, so the impact start and end time can be inferred. Results also show the short-term impact of COVID-19 is similar across different regions, while the long-term impact is not. In some regions, the pandemic only caused a time-shift in electricity consumption; but in others, the electricity consumption pattern has been permanently changed. The data-driven analysis of this paper can be the first step to fully interpret the COVID-19 impact by considering economic and social parameters in future studies.

scholarly journals Bottom-up Long-term Forecasting of Brazilian Commercial Class Electricity Consumption: First Results

2015 ◽  
Vol 55 ◽  
pp. 388-394 ◽  
Author(s):  
Bruno Q. Bastos ◽  
Reinaldo C. Souza ◽  
Fernando L. Cyrino Oliveira
Keyword(s):  
Electricity Consumption ◽  
Bottom Up ◽  
First Results ◽  
Long Term Forecasting

scholarly journals Recurrent Deep Multiagent Q-Learning for Autonomous Brokers in Smart Grid

2018 ◽  
Author(s):  
Yaodong Yang ◽  
Jianye Hao ◽  
Mingyang Sun ◽  
Zan Wang ◽  
Changjie Fan ◽  
...  
Keyword(s):  
Smart Grid ◽  
Electricity Consumption ◽  
Superior Performance ◽  
Pricing Strategy ◽  
Retail Market ◽  
Demand And Supply ◽  
Q Learning ◽  
Consumption Data ◽  
Reward Shaping

The broker mechanism is widely applied to serve for interested parties to derive long-term policies in order to reduce costs or gain profits in smart grid. However, a broker is faced with a number of challenging problems such as balancing demand and supply from customers and competing with other coexisting brokers to maximize its profit. In this paper, we develop an effective pricing strategy for brokers in local electricity retail market based on recurrent deep multiagent reinforcement learning and sequential clustering. We use real household electricity consumption data to simulate the retail market for evaluating our strategy. The experiments demonstrate the superior performance of the proposed pricing strategy and highlight the effectiveness of our reward shaping mechanism.

Long term forecasting of hourly electricity consumption in local areas in Denmark

2013 ◽  
Vol 110 ◽  
pp. 147-162 ◽  
Author(s):  
F.M. Andersen ◽  
H.V. Larsen ◽  
R.B. Gaardestrup
Keyword(s):  
Electricity Consumption ◽  
Local Areas ◽  
Long Term Forecasting

scholarly journals Changes in Electricity Load Profiles Under COVID-19: Implications of “The New Normal” for Electricity Demand

2020 ◽  
Author(s):  
Casey Burleyson ◽  
Amanda D. Smith ◽  
Jennie S. Rice ◽  
Nathalie Voisin ◽  
Aowabin Rahman
Keyword(s):  
Structural Changes ◽  
Electricity Consumption ◽  
Investment Decisions ◽  
Total Load ◽  
Electricity Use ◽  
Consumption Data ◽  
Electricity Load ◽  
Potential Impact ◽  
Grid Operators

Shelter-in-place orders and school and business closures related to COVID-19 have changed the hourly profile of electricity loads in the U.S. Such shifts have significant implications for utilities and grid operators, affecting operational efficiency as well as investment decisions. It is critical to understand if and how these changes may persist as economies gradually reopen. Using 2 years of observed electricity consumption data from more than 3.8 million residential and non-residential customers from the Commonwealth Edison utility in Illinois, we show that the onset of COVID-19 shifted weekday residential load profiles to closely resemble weekend profiles from previous years. We use this finding to estimate the potential impact of continued COVID-19-type profiles of electricity use on total load profiles. We find that long-term structural changes to the workplace like widespread teleworking could lead to 5-7% higher spring and summertime peak hourly loads occurring up to 2.5 hours earlier.

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