Electricity Price Prediction Using Machine Learning

Forecasting electricity prices is an essential approach for energy market members like customers, traders and sellers. The effective prediction enables proper decision making based on energy usage, buying and selling. We guide you by selecting the right impactful topic for machine learning where we assure your first step to success. We guide you by writing original and new concept and provide novel ideas for all your machine learning research work queries.. With our expert support you invest a strong pathway for reputed journal publication rather that availing only our services.

We list out the procedural steps for developing electricity price prediction framework by utilizing machine learning:

  1. Objective Description:
  • Short-term vs. Long-term: Ensure whether we are forecasting prices for the upcoming hour, day, week, month or year.
  • Granularity: In our work, we evaluate the granularity of our forecasting, for instance: hourly, daily or weekly basis.
  1. Data Gathering:
  • Historical prices: Our research collects previous electricity price data and we consider this as an initial phase for forecasting.
  • Demand & Supply Data: we demonstrate that, electricity price connects with requirement and supply data frequently.
  • Renewable Generation: With important renewable penetration, data related to wind, solar or hydro production helps us to carry out this project.
  • Other Factors: Various data based on significant incidents, weather or holidays have major contributions in prices.
  1. Preprocessing of data:
  • Missing data: By utilizing imputation, interpolation and removal processes, we manage the missing data.
  • Feature Engineering: Our work produces novel characteristics. For example: moving averages, lagged prices (such as prices for past days or hours), and various measures for particular incidents or holidays.
  • Normalization of Data: Specifically for the frameworks such as Neural Networks or SVM, which are vulnerable to feature scales, we normalize or standardize features to attain best framework efficiency.
  • Splitting of Data: Our research divides the data into different sets like training, validation and test data.
  1. Model Chosen & Training:
  • Conventional ML Models: We make use of various regression methods including Decision Trees, Gradient Boosting Machines, Random forest, Linear Regression and SVM.
  • Time Series Models: When dealing with time series data, our project considers several techniques like ARIMA, Prophet or Exponential Smoothing State Space Models (ETS).
  • Deep Learning: In this, we use RNNs, specifically LSTMs or GRU assist us to capture temporal connections in time series data.
  • Ensembling: To obtain efficient accuracy, our research integrates forecasting from different frameworks.
  1. Evaluation:
  • Metrics: We examine the forecasting accuracy in terms of various metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE) or Root Mean Squared Error (RMSE).
  • Visualization: To visually evaluate the efficiency of our framework, plot and compare the forecasted and actual prices.
  • Back-testing: In actual-time selling or purchasing environments, we test the performance of our framework’s forecasting.
  1. Deployment:
  • Web/Application Interface: To know the forecasted prices or to get notifications, we provide an interface to share-holders.
  • Real-time Integration: Implement our framework on actual-world platforms to offer regular updates for short-term forecasting.

Project Extensions:

  1. Anomaly Identification: We detect and notify on abnormal increments in prices.
  2. Price Elasticity Modeling: To predict demand reaction to price modifications, our framework integrates price elasticity.
  3. Integrate with Optimization Systems: To direct decision making process in energy based business, demand reaction model or storage dispatch, we utilize forecasting.


  • Non-stationarity: Methods such as differencing or transformations assist us to deal with electricity prices because this is a dynamic factor which means their static characteristics modify periodically.
  • Regime Shifts: We experience adverse changes in price trends due to the alterations in market regulations, significant environment additions such as new power plants or some important economic incidents.
  • High Volatility: there may be changes in prices certainly in actual-time or daily businesses, because of various attributes such as renewable irregularity or unanticipated demand alterations.

On the basis of various factors like geography, regulatory platform and market infrastructure, the electricity business may change specifically. We state that it is very important to interpret certain features of markets we are dealing with and appropriately tune the techniques. Improve the robustness of our framework by associating with specific research professionals.

Our experts span disciplines across assistance to scholars in all domains of machine learning. We at phddirection.com provide on time delivery for your research manuscript under Electricity Price Prediction our team stays updated on all emerging disciplines.

Electricity Price Prediction Using Machine Learning Topics

Electricity Price Prediction Using Machine Learning Thesis Ideas

At phddirection.org we understand how much difficulties scholars face at the time of writing thesis. So, we provide an array of services that might interest you. Thesis Ideas, Thesis Topics, Thesis Proposal and Thesis Writing on all Machine Learning topics are written by us. A complete explanation will be given, moreover modifications can be done as per scholars wish.

  1. Electricity Price Prediction using Machine Learning


Support vector machines, Measurement, Machine learning algorithms, Artificial neural networks, Production, Predictive models, Prediction algorithms

            We enhanced to predict electricity by using ML methods. Prediction of electricity price can depend on various factors such as national wind, wind production and natural factors etc.. We used four types of regressors namely Random Forest Regression, Logistic Regression, Support Vector Regressor, and Artificial Neural Network Regressor to evaluate the price. We also used MAE as performance metric. ANN performs better outcome.   

  1. Short-term Electricity Price Prediction Using Kernel-based Machine Learning Techniques


Power engineering, Systematics, Measurement units, Power system dynamics, Neurons, Kernel

            Predicting the Market clearing price (MCP) in markets is crucial task to increase the values of both supplier and consumer. This can be achieved by using neural network based prediction method. The Extreme Learning Method (ELM) is compared with advanced kernel- based technique. We also focused on prediction algorithm as more weight interval prediction.

  1. Machine Learning Based Prediction and Forecasting of Electricity Price During COVID-19


COVID-19, Deep learning, Temperature distribution, Recurrent neural networks, Uncertainty, Pandemics

             In this work we analyse electricity price and prediction is carried out on the Wholesale market of United States namely MISO electricity market. We proposed three models namely Auto Regressive Integrated Moving Average (ARIMA), Extreme Gradient Boosting (XGBoost) and Recurrent Neural Network (RNN) to predict the electricity price.

4. Prediction of Electricity Bill using Supervised Machine Learning Technique


Costs, Government, Electric variables measurement, Market research, Admittance

            Time series data and ML technique can be used to predict electricity cost but it is difficult to predict. We aim to pick the better way to apply electrical design to get better result. AI technology separates the informational collection to get data based on changes, single-variable examination, two-fold factor and different breaks down. AI calculation can estimate the proper calculation: MAE, MSE, R2.

  1. Machine Learning Models and Intra-Daily Market Information for the Prediction of Italian Electricity Prices


Electricity spot prices; forecasting; intra-day electricity prices; random forests; variable importance

            We can evaluate how intra-day electricity prices increase the prediction of zonal day-ahead wholesale electricity prices in Italy. We consider linear autoregressive models with exogenous variables (ARX) and non-parametric model from ML. We also used SVM and RF which capture corresponding communication among predictor. Large number of predictors and ARX models are calculated using LASSO regularization.

  1. Electricity Price Fundamentals in Hydrothermal Power Generation Markets Using Machine Learning and Quantile Regression Analysis


Hydrothermal Power Generation Markets, Quantile Regression, Gaussian Process Regression JEL Classifications: C22, Q41, Q43, Q47

            We propose an empirical method to evaluate the price determinants and their impact on price dynamics. This paper gives two methodologies: In first method we evaluate the price determinants through prediction and in the second the quantile regression is used to verify non-linear effects. The most important elements are total market demand, water reservoirs capacity for generation, and fossil fuel consumption.

  1. A machine learning approach for electricity future price prediction


Power market, futures market, Nordic power market, LSTM, TCN

            In this paper two ML models can be used namely LSTM and TCN to electricity future contract. Future contract can secure the price of electricity in future. A multivariate time series of data that corresponds with electricity prices is used as input for predicting. The multivariate TCN model performs better than LSTM.

  1. Electricity Consumption & Prediction using Machine Learning Models


Electricity Consumption, Electricity Prediction, Conventional Machine Learning, Adaptive Network based Fuzzy Inference System (ANFIS), Extreme Learning Machine (ELM)

                                  In this paper we analyse ML algorithms use after applied to the electricity consumption prediction. Our aim is to give important guidelines to ML community and give basic information about electricity valuation method for ML algorithms. This paper also reviews Conventional ML methods that allow prediction of electricity consumption.  

  1. Prediction of Electricity Bill Using SML Technique


Predict electricity bill, pre-processing, regression algorithm, feature extraction

                   The goal of this paper is to predict electricity price by using ML based method. The dataset can be analysed by using supervised machine learning technique (SMLT) to collect information’s like variable identification, uni-variate, bivariate and multi variate analysis, cleaning, analyse the data validation will be done on the overall dataset. our proposed ML method can be compared with MAE,MSE and R2 to give better accuracy. 

  1. Day-Ahead Market Electricity Price Prediction using Time Series Forecasting


Recurrent neural networks, Statistical analysis, Time series analysis, Inspection, Electricity supply industry, Forecasting

               We present a comparative analysis of diverse price methods. In this paper different methods are compared Statistical method (auto regressive integrated moving average), ML method (Random Forest regression) and in Novel DL (LSTM-ANN) can be used. These methods are used for predicting electricity prices.

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