Energy Consumption, Demand and Price Forecasting with Artificial Intelligence

Topic Information

Dear Colleagues,

Over the last two decades, electricity demand and price forecasting has become a fundamental decision-making tool for governments and energy companies. As electricity cannot be stored like other energies such as natural gas, predictive methods to forecast their use and production needs should be developed subject to changing energy demands. All factors of supply and demand will, therefore, have an immediate impact on the price of electricity on the spot market, including profits, energy security, and energy supply risk management. In addition to energy production cost, electricity price is determined by the changing nature of the supply and the consumer demand.

This Topic will focus on energy science and engineering or related research on electricity, gas and other forms of energy consumption prediction, demand, and price forecasting with artificial intelligence. It aims to publish cutting-edge, latest research, analyses, reviews, and evaluations related to energy demand, energy price and electricity load with a strong focus on analysis, energy modelling and prediction, integrated renewable and conventional energy system, energy planning and management systems powered by artificial intelligence. The Topic welcomes original papers, extensive reviews, critical insights, or exploratory scientific studies related to energy conservation, energy efficiency, renewable energy, electricity supply and demand, predicting energy storage methods, predicting energy load in buildings, and energy economics and policy issues. We are interested in multi-agent methods to provide insights as to whether demand or prices will be above marginal and how this might influence consumer behavior.

This Topic welcomes novel research on integrated energy consumption or demand side management and price forecasting or capabilities that consider methodologies like data analytics, data science, predictive models, experimental, analysis and optimization with a verification of all methods or application challenges. The topics may include:

• Deep learning and artificial intelligence methods for electricity demand and price prediction. This includes computational intelligence (machine learning, non-parametric, non-linear statistical) methods with learning, evolutionary or fuzziness methods capable of adapting to complex dynamic energy use, price, and demand changes.
• Multi-agent, multi-agent simulation, equilibrium, game theoretic models used to simulate energy price by matching demand and supply. Methods could include cost-based (or production-cost) models, equilibrium, or game theoretic approaches (such as the Nash–Cournot framework, supply function equilibrium, strategic production-cost models, and agent-based models).
• Fundamental and structural methods: physical and economic relationship analysis for electricity production or trading including the associations between fundamental drivers such as load variations due to weather conditions; system parameter changes, etc., and fundamental inputs modeled or predicted independently using statistical, reduced form and computational intelligence.
• Reduced form models (quantitative, stochastic) to characterize statistical properties of electricity price over time for risk management. This may provide hourly or minute time-scale demand and price forecasts using main characteristics of daily or other time-scale electricity prices, marginal distributions, price dynamics or correlation between commodity prices. These include Spot price models (parsimonious representation of the dynamics of spot prices) or forward price models(pricing of derivatives in a straightforward manner but only of those written on the forward price of electricity).
• Statistical (econometric, technical) methods developed to forecast loads or price using mathematical combination of price or exogenous factors like consumption or production or weather variables.
• Complex network analysis for electricity production and demand, relations from different local areas, distribution, and planning. Study of the correlation among the different energy sources.
• Probabilistic prediction of electricity demand using the deep transformer model.
• Analysis of electricity production and demand.

The Topic welcomes papers on energy consumption, demand and price forecasting over a range of horizons such as short-term forecasting (from a few minutes to a few days ahead as being of prime importance in day-to-day market operations), medium-term forecasting (days to a few months ahead, is generally preferred for balance sheet calculations, risk management and derivatives pricing) and long-term forecasting (predicted months, quarters or even year ahead to focus on long-term investment profitability analysis or energy planning, future electricity production site and other analysis). Any new methods developed and tested for economic and financial impact analysis studies on National Electricity Markets are especially welcome.

Prof. Dr. Ravinesh Deo
Prof. Dr. Sancho Salcedo-Sanz
Dr. Sujan Ghimire
Dr. David Casillas Pérez
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	-	-	2020	20.8 Days	CHF 1600
Electricity electricity	-	-	2020	20.3 Days	CHF 1000
Energies energies	3.2	5.5	2008	16.1 Days	CHF 2600
Environments environments	3.7	5.9	2014	23.7 Days	CHF 1800
Sustainability sustainability	3.9	5.8	2009	18.8 Days	CHF 2400

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Published Papers (8 papers)

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25 pages, 3545 KiB  

Open AccessArticle

Short-Term Natural Gas and Carbon Price Forecasting Using Artificial Neural Networks

by Laura Böhm, Sebastian Kolb, Thomas Plankenbühler, Jonas Miederer, Simon Markthaler and Jürgen Karl

Energies 2023, 16(18), 6643; https://doi.org/10.3390/en16186643 - 15 Sep 2023

Viewed by 941

Abstract

Methods of computational intelligence show a high potential for short-term price forecasting of the energy market as they offer the possibility to cope with the complexity, multi-parameter dependency, and non-linearity of pricing mechanisms. While there is a large number of publications applying neural [...] Read more.

Methods of computational intelligence show a high potential for short-term price forecasting of the energy market as they offer the possibility to cope with the complexity, multi-parameter dependency, and non-linearity of pricing mechanisms. While there is a large number of publications applying neural networks to the prediction of electricity prices, the analysis of natural gas and carbon prices remains scarce. Identifying a best practice from the literature, this study presents an iterative approach to optimize both the input values and network configuration of neural networks. We apply the approach to the natural gas and carbon market, sequentially testing autoregressive and exogenous explanatory variables as well as different neural network architectures. We subsequently discuss the influence of architectural properties, input parameters, data preparation, and the models’ resilience to singular events. Results show that the selection of appropriate lags of gas and carbon prices to account for autoregressive properties of the respective time series leads to a high degree of forecasting accuracy. Additionally, including ambient temperature data can slightly reduce errors of natural gas price forecasting whereas carbon price predictions benefit from electricity prices as a further explanatory input. The best configurations presented in this contribution achieve a root mean square error (RMSE) of 0.64 EUR/MWh (natural gas prices) corresponding to a normalized RMSE of 0.037 and 0.33 EUR/t_CO2 (carbon prices) corresponding to a normalized RMSE of 0.023. Full article

(This article belongs to the Topic Energy Consumption, Demand and Price Forecasting with Artificial Intelligence)

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19 pages, 5104 KiB  

Open AccessArticle

Dynamic Pricing Based on Demand Response Using Actor–Critic Agent Reinforcement Learning

by Ahmed Ismail and Mustafa Baysal

Energies 2023, 16(14), 5469; https://doi.org/10.3390/en16145469 - 19 Jul 2023

Viewed by 1306

Abstract

Eco-friendly technologies for sustainable energy development require the efficient utilization of energy resources. Real-time pricing (RTP), also known as dynamic pricing, offers advantages over other pricing systems by enabling demand response (DR) actions. However, existing methods for determining and controlling DR have limitations [...] Read more.

Eco-friendly technologies for sustainable energy development require the efficient utilization of energy resources. Real-time pricing (RTP), also known as dynamic pricing, offers advantages over other pricing systems by enabling demand response (DR) actions. However, existing methods for determining and controlling DR have limitations in managing an increasing demand and predicting future pricing. This paper presents a novel approach to address the limitations of existing methods for determining and controlling demand response (DR) in the context of dynamic pricing systems for sustainable energy development. By leveraging actor–critic agent reinforcement learning (RL) techniques, a dynamic pricing DR model is proposed for efficient energy management. The model’s learning framework was trained using DR and real-time pricing data extracted from the Australian Energy Market Operator (AEMO) spanning a period of 17 years. The efficacy of the RL-based dynamic pricing approach was evaluated through two predicting cases: actual-predicted demand and actual-predicted price. Initially, long short-term memory (LSTM) models were employed to predict price and demand, and the results were subsequently enhanced using the deep RL model. Remarkably, the proposed approach achieved an impressive accuracy of 99% for every 30 min future price prediction. The results demonstrated the efficiency of the proposed RL-based model in accurately predicting both demand and price for effective energy management. Full article

(This article belongs to the Topic Energy Consumption, Demand and Price Forecasting with Artificial Intelligence)

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19 pages, 4530 KiB  

Open AccessArticle

Prediction of Building Electricity Consumption Based on Joinpoint−Multiple Linear Regression

by Hao Yang, Maoyu Ran and Chaoqun Zhuang

Energies 2022, 15(22), 8543; https://doi.org/10.3390/en15228543 - 15 Nov 2022

Cited by 3 | Viewed by 1639

Abstract

Reliable energy consumption forecasting is essential for building energy efficiency improvement. Regression models are simple and effective for data analysis, but their practical applications are limited by the low prediction accuracy under ever-changing building operation conditions. To address this challenge, a Joinpoint–Multiple Linear [...] Read more.

Reliable energy consumption forecasting is essential for building energy efficiency improvement. Regression models are simple and effective for data analysis, but their practical applications are limited by the low prediction accuracy under ever-changing building operation conditions. To address this challenge, a Joinpoint–Multiple Linear Regression (JP–MLR) model is proposed in this study, based on the investigation of the daily electricity usage data of 8 apartment complexes located within a university in Xiamen, China. The univariate model is first built using the Joinpoint Regression (JPR) method, and then the remaining residuals are evaluated using the Multiple Linear Regression (MLR) method. The model contains six explanatory variables, three of which are continuous (mean outdoor air temperature, mean relative humidity, and temperature amplitude) and three of which are categorical (gender, holiday index, and sunny day index). The performance of the JP–MLR model is compared to that of the other four data-driven algorithm models: JPR, MLR, Back Propagation (BP) neural network, and Random Forest (RF). The JP–MLR model, which has an R² value of 95.77%, has superior prediction performance when compared to the traditional regression-based JPR model and MLR model. It also performs better than the machine learning-based BP model and is identical to that of the RF model. This demonstrates that the JP–MLR model has satisfactory prediction performance and offers building operators an effective prediction tool. The proposed research method also provides also serves as a reference for electricity consumption analysis in other types of buildings. Full article

(This article belongs to the Topic Energy Consumption, Demand and Price Forecasting with Artificial Intelligence)

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19 pages, 10715 KiB  

Open AccessArticle

An Adaptive Strategy for Medium-Term Electricity Consumption Forecasting for Highly Unpredictable Scenarios: Case Study Quito, Ecuador during the Two First Years of COVID-19

by Manuel Jaramillo and Diego Carrión

Energies 2022, 15(22), 8380; https://doi.org/10.3390/en15228380 - 09 Nov 2022

Cited by 5 | Viewed by 1386

Abstract

This research focuses its efforts on the prediction of medium-term electricity consumption for scenarios of highly variable electricity demand. Numerous approaches are used to predict electricity demand, among which the use of time series (ARMA, ARIMA) and the use of machine learning techniques, [...] Read more.

This research focuses its efforts on the prediction of medium-term electricity consumption for scenarios of highly variable electricity demand. Numerous approaches are used to predict electricity demand, among which the use of time series (ARMA, ARIMA) and the use of machine learning techniques, such as artificial neural networks, are the most covered in the literature review. All these approaches evaluate the prediction error when comparing the generated models with the data that fed the model, but they do not compare these values with the actual data of electricity demand once these are obtained, in addition, these techniques present high error values when there are unexpected changes in the trend of electricity consumption. This work proposes a methodology to generate an adaptive model for unexpected changes in electricity demand through the use of optimization in conjunction with $SARIMA$ time series. The proposed case study is the electricity consumption in Quito, Ecuador to predict the electricity demand in the years 2019 and 2020, which are particularly challenging due to atypical electricity consumption attributed to COVID-19. The results show that the proposed model is capable of following the trend of electricity demand, adapting itself to sudden changes and obtaining an average error of 2.5% which is lower than the average error of 5.43% when using a non-adaptive approach (more than 50% or error improvement). Full article

(This article belongs to the Topic Energy Consumption, Demand and Price Forecasting with Artificial Intelligence)

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18 pages, 3542 KiB  

Open AccessArticle

Exploration of Urban Emission Mitigation Pathway under the Carbon Neutrality Target: A Case Study of Beijing, China

by Zheng Jiang, Shuohua Zhang and Wei Li

Sustainability 2022, 14(21), 14016; https://doi.org/10.3390/su142114016 - 27 Oct 2022

Cited by 1 | Viewed by 1252

Abstract

Exploring the urban carbon neutrality pathway is crucial to the overall achievement of the net-zero emissions target in China. Therefore, taking Beijing as a case study, this paper firstly analyzes the CO₂ emission drivers by combining the Stochastic Impacts by Regression on [...] Read more.

Exploring the urban carbon neutrality pathway is crucial to the overall achievement of the net-zero emissions target in China. Therefore, taking Beijing as a case study, this paper firstly analyzes the CO₂ emission drivers by combining the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) and partial least squares (PLS) methods. Subsequently, based on the optimized extreme learning machine (ELM) model, this paper projects the CO₂ emissions of Beijing during 2021–2060 under different scenarios. The results show that controlling the total energy consumption and increasing the proportion of non-fossil energy consumption and electrification level should be the key measures to implement emission reduction in Beijing. Particularly, the proportion of non-fossil energy consumption and electrification level should be increased to 65% and 73%, respectively, in 2060. In addition, more stringent emission reduction policies need to be implemented to achieve the carbon neutrality target. Under the H−EPS scenario, Beijing’s CO₂ emissions peaked in 2010 and will be reduced by a cumulative 109 MtCO₂ during 2021–2060. Along with executing emission mitigation policies, Beijing should actively increase carbon sinks and develop carbon capture, utilization, and storage (CCUS) technology. Especially after 2040, the emission reduction produced by carbon sinks and CCUS technology should be no less than 20 MtCO₂ per year. Full article

(This article belongs to the Topic Energy Consumption, Demand and Price Forecasting with Artificial Intelligence)

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21 pages, 5276 KiB  

Open AccessArticle

Spatiotemporal Prediction of Urban Online Car-Hailing Travel Demand Based on Transformer Network

by Shuoben Bi, Cong Yuan, Shaoli Liu, Luye Wang and Lili Zhang

Sustainability 2022, 14(20), 13568; https://doi.org/10.3390/su142013568 - 20 Oct 2022

Cited by 3 | Viewed by 1672

Abstract

Online car-hailing has brought convenience to daily travel, whose accurate prediction benefits drivers and helps managers to grasp the characteristics of urban travel, so as to facilitate decisions. Spatiotemporal prediction in the transportation field has usually been based on a recurrent neural network [...] Read more.

Online car-hailing has brought convenience to daily travel, whose accurate prediction benefits drivers and helps managers to grasp the characteristics of urban travel, so as to facilitate decisions. Spatiotemporal prediction in the transportation field has usually been based on a recurrent neural network (RNN), which has problems such as lengthy computation and backpropagation. This paper describes a model based on a Transformer, which has shown success in computer vision. The study area is divided into grids, and the structure of travel data is converted into video frames by time period, based on predicted spatiotemporal travel demand. The predictions of the model are closest to the real data in terms of spatial distribution and travel demand when the data are divided into 10 min intervals, and the travel demand in the first two hours is used to predict demand in the next hour. We experimentally compare the proposed model with the three most commonly used spatiotemporal prediction models, and the results show that our model has the best accuracy and training speed. Full article

(This article belongs to the Topic Energy Consumption, Demand and Price Forecasting with Artificial Intelligence)

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16 pages, 2886 KiB  

Open AccessEditor’s ChoiceArticle

Wholesale Electricity Price Forecasting Using Integrated Long-Term Recurrent Convolutional Network Model

by Vasudharini Sridharan, Mingjian Tuo and Xingpeng Li

Energies 2022, 15(20), 7606; https://doi.org/10.3390/en15207606 - 14 Oct 2022

Cited by 18 | Viewed by 1964

Abstract

Electricity price forecasts have become a fundamental factor affecting the decision-making of all market participants. Extreme price volatility has forced market participants to hedge against volume risks and price movements. Hence, getting an accurate price forecast from a few hours to a few [...] Read more.

Electricity price forecasts have become a fundamental factor affecting the decision-making of all market participants. Extreme price volatility has forced market participants to hedge against volume risks and price movements. Hence, getting an accurate price forecast from a few hours to a few days ahead is very important and very challenging due to various factors. This paper proposes an integrated long-term recurrent convolutional network (ILRCN) model to predict electricity prices considering the majority of contributing attributes to the market price as input. The proposed ILRCN model combines the functionalities of a convolutional neural network and long short-term memory (LSTM) algorithm along with the proposed novel conditional error correction term. The combined ILRCN model can identify the linear and nonlinear behavior within the input data. ERCOT wholesale market price data along with load profile, temperature, and other factors for the Houston region have been used to illustrate the proposed model. The performance of the proposed ILRCN electricity price forecasting model is verified using performance/evaluation metrics like mean absolute error and accuracy. Case studies reveal that the proposed ILRCN model shows the highest accuracy and efficiency in electricity price forecasting as compared to the support vector machine (SVM) model, fully connected neural network model, LSTM model, and the traditional LRCN model without the conditional error correction stage. Full article

(This article belongs to the Topic Energy Consumption, Demand and Price Forecasting with Artificial Intelligence)

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17 pages, 3550 KiB  

Open AccessArticle

Optimization of a Renewable Energy Source-Based Virtual Power Plant for Electrical Energy Management in an Unbalanced Distribution Network

by T. Kesavan and K. Lakshmi

Sustainability 2022, 14(18), 11129; https://doi.org/10.3390/su141811129 - 06 Sep 2022

Cited by 6 | Viewed by 1904

Abstract

The virtual power plant (VPP) is a developing concept in the modern engineering field. This paper presents a local search optimization (LSO) algorithm-based virtual power plant for energy management in a distribution network. The proposed LSO algorithm is used for the optimal selection [...] Read more.

The virtual power plant (VPP) is a developing concept in the modern engineering field. This paper presents a local search optimization (LSO) algorithm-based virtual power plant for energy management in a distribution network. The proposed LSO algorithm is used for the optimal selection and location of the distributed energy resources (DER), the optimal regulation of load, and the optimal usage of energy storage systems in a VPP. DERs are a renewable energy sources (RES) that consist of solar PV and a wind energy source. DERs face the challenge of energy losses, voltage variations, and revenue losses in the utilization network. These problems are solved by the proposed VPP concept by reducing the acquiring of energy from the power sector. An LSO-based virtual power plant is modeled in MATLB PSCAD and verified using the IEEE-9 bus system. The results show that 81% of the purchased energy from the utility grid was reduced by the optimal placement of the DER and 86% of acquired energy from utility grid was reduced by the optimal location of the DER and optimal load control in the VPP. Full article

(This article belongs to the Topic Energy Consumption, Demand and Price Forecasting with Artificial Intelligence)

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