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:
- 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.
- 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.
- 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.
- 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.
- 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.
- 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:
- Anomaly Identification: We detect and notify on abnormal increments in prices.
- Price Elasticity Modeling: To predict demand reaction to price modifications, our framework integrates price elasticity.
- Integrate with Optimization Systems: To direct decision making process in energy based business, demand reaction model or storage dispatch, we utilize forecasting.
Challenges:
- 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.
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Electricity Price Prediction Using Machine Learning Thesis Ideas
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