scholarly journals Electricity Usage Efficiency and Electricity Demand Modeling in the Case of Germany and the UK

2020 ◽  
Vol 10 (7) ◽  
pp. 2291 ◽  
Author(s):  
Branislav Dudic ◽  
Jan Smolen ◽  
Pavel Kovac ◽  
Borislav Savkovic ◽  
Zdenka Dudic
Keyword(s):  
Demand Forecasting ◽  
Electricity Consumption ◽  
Electricity Demand ◽  
Demand Modeling ◽  
Multiple Variable ◽  
Demand Forecasting Model ◽  
Multiple Variable Regression ◽  
The Uk ◽  
Combined Impact

In this article, monthly and yearly electricity consumption predictions for the German power market were calculated using the multiple variable regression model. This model accounts for several factors that are often neglected when forecasting electricity demand in practice, in particular the role of the higher efficiency of electricity usage from year to year. The analysis performed in this paper helps to explain why no growth in power consumption has been observed in Germany during the last decade. It shows that the electricity efficiency usage dataset is a relevant input for the model, which mitigates the combined impact of other factors on the final electricity consumption. The electricity demand forecasting model presented in this article was built in the year 2013 with forecasts for the future years’ electricity demand in Germany provided until 2020. These forecasts and related findings are also evaluated in this article.

scholarly journals Hierarchical Electricity Time Series Forecasting for Integrating Consumption Patterns Analysis and Aggregation Consistency

2018 ◽  
Author(s):  
Yue Pang ◽  
Bo Yao ◽  
Xiangdong Zhou ◽  
Yong Zhang ◽  
Yiming Xu ◽  
...  
Keyword(s):  
Time Series ◽  
Demand Forecasting ◽  
Real Life ◽  
Forecast Accuracy ◽  
Electricity Consumption ◽  
Electricity Demand ◽  
Time Series Forecasting ◽  
Consumption Patterns ◽  
Consumption Pattern ◽  
Bottom Level

Electricity demand forecasting is a very important problem for energy supply and environmental protection. It can be formalized as a hierarchical time series forecasting problem with the aggregation constraints according to the geographical hierarchy, since the sum of the prediction results of the disaggregated time series should be equal to the prediction results of the aggregated ones. However in most previous work, the aggregation consistency is ensured at the loss of forecast accuracy. In this paper, we propose a novel clustering-based hierarchical electricity time series forecasting approach. Instead of dealing with the geographical hierarchy directly, we explore electricity consumption patterns by clustering analysis and build a new consumption pattern based time series hierarchy. We then present a novel hierarchical forecasting method with consumption hierarchical aggregation constraints to improve the electricity demand predictions of the bottom level, followed by a ``bottom-up" method to obtain forecasts of the geographical higher levels. Especially, we observe that in our consumption pattern based hierarchy the reconciliation error of the bottom level time series is ``correlated" to its membership degree of the corresponding cluster (consumption pattern), and hence apply this correlations as the regularization term in our forecasting objective function. Extensive experiments on real-life datasets verify that our approach achieves the best prediction accuracy, compared with the state-of-the-art methods.

scholarly journals Primary energy sources planning based on demand forecasting: The case of Turkey

2016 ◽  
Vol 27 (1) ◽  
pp. 2 ◽  
Author(s):  
Coşkun Hamzaçebi
Keyword(s):  
Demand Forecasting ◽  
Electricity Consumption ◽  
Primary Energy ◽  
Electricity Demand ◽  
Electricity Production ◽  
Energy Sources ◽  
Original Form ◽  
Grey Forecasting ◽  
Preliminary Information ◽  
Proposed Model

Forecasting electricity consumption is a very important issue for governments and electricity related foundations of public sector. Recently, Grey Modelling (GM (1,1)) has been used to forecast electricity demand successfully. GM (1,1) is useful when the observed data is limited, and it does not require any preliminary information about the data distribution. However, the original form of GM (1,1) needs some improvements in order to use for time series, which exhibit seasonality. In this study, a grey forecasting model which is called SGM (1,1) is proposed to give the forecasting ability to the basic form of GM(1,1) in order to overcome seasonality issues. The proposed model is then used to forecast the monthly electricity demand of Turkey between 2015 and 2020. Obtained forecasting values were used to plan the primary energy sources of electricity production. The findings of the study may guide the planning of future plant investments and maintenance operations in Turkey. Moreover, the method can also be applied to predict seasonal electricity demand of any other country.

scholarly journals Application of LEAP model on long-term electricity demand forecasting in Indonesia, period 2010-2025

2018 ◽  
Vol 49 ◽  
pp. 02007 ◽  
Author(s):  
Jaka Windarta ◽  
Bambang Purwanggono ◽  
Fuad Hidayanto
Keyword(s):  
Demand Forecasting ◽  
Electricity Consumption ◽  
Electricity Demand ◽  
Planning System ◽  
Large Country ◽  
Electricity Demand Forecasting ◽  
Electricity Planning ◽  
Business As Usual

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.

scholarly journals Day-Ahead Prediction of Microgrid Electricity Demand Using a Hybrid Artificial Intelligence Model

Processes ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 320 ◽  
Author(s):  
Yuan-Jia Ma ◽  
Ming-Yue Zhai
Keyword(s):  
Artificial Intelligence ◽  
Optimization Technique ◽  
Demand Forecasting ◽  
Electricity Consumption ◽  
Electricity Demand ◽  
Small Error ◽  
Data Series ◽  
Time Interval ◽  
Electric Load ◽  
Demand Forecast

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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