Improving Short-Term Electricity Price Forecasting Using Day-Ahead LMP with ARIMA Models

Short-term electricity price forecasting has become important for demand side management and power generation scheduling. Especially as the electricity market becomes more competitive, a more accurate price prediction than the day-ahead locational marginal price (DALMP) published by the independent system operator (ISO) will benefit participants in the market by increasing profit or improving load demand scheduling. Hence, the main idea of this paper is to use autoregressive integrated moving average (ARIMA) models to obtain a better LMP prediction than the DALMP by utilizing the published DALMP, historical real-time LMP (RTLMP) and other useful information. First, a set of seasonal ARIMA (SARIMA) models utilizing the DALMP and historical RTLMP are developed and compared with autoregressive moving average (ARMA) models that use the differences between DALMP and RTLMP on their forecasting capability. A generalized autoregressive conditional heteroskedasticity (GARCH) model is implemented to further improve the forecasting by accounting for the price volatility. The models are trained and evaluated using real market data in the Midcontinent Independent System Operator (MISO) region. The evaluation results indicate that the ARMAX-GARCH model, where an exogenous time series indicates weekend days, improves the short-term electricity price prediction accuracy and outperforms the other proposed ARIMA modelsComment: IEEE PES 2017 General Meeting, Chicago, I

Spot Price Forecasting : Evaluating the Impact of Weather Based Demand Forecasting on Electricity Market Predictions

This thesis uses electricity data sourced from Nord Pool and weather data obtained from Norsk Klimaservicesenter, seeking to forecast day-ahead spot prices by leveraging temperature-based demand forecasts. Through this analysis, we aim to examine the feasibility of developing a model that can be utilised by participants in the electricity market bidding process. A significant portion of our research efforts has been dedicated to exploring a SARIMAX model, which is widely employed in this field of research. However, we have also thoroughly examined and tested various alternative models to assess their viability by considering them as potential benchmarks. The thesis is structured into several chapters, beginning with an initial introduction that provides an overview of the electricity market in Norway. This section serves to establish the context and background for our research. Following the introduction, we delve into the presentation of the data and methods employed to address our research question. This chapter outlines the specific datasets utilised and the methodologies implemented in our analysis. Finally, we conclude the thesis by presenting our results and the implications our study might have for the participants in the Nord Pool day-ahead market. Our findings reveal a notable spurious correlation between temperature and spot price. However, we acknowledge that relying solely on weather variables is insufficient due to the influence of external factors on pricing decisions. Nevertheless, our research has yielded satisfactory results, with the best models achieving an overall error ranging between 5-10%. Our main model consistently performed well, although there were instances where alternative models outperformed it on specific days or weeks.nhhma

Short-term load forecasting in times of unprecedented price movements

In this thesis we aimed to find the best methods for short-term load forecasting in the Norwegian electricity market during times of unprecedented price movements. We answered three questions related to this aim. The first was which model achieved the most accurate forecast. The second was whether our proposed models outperform the official forecasts published on the Entso-E platform. The third question asked was if the price movements had any effect on the accuracy of the load forecast. We constructed two SARIMAX models, a Gradient boosted decision tree, a Random Forest, and a Multilayer perceptron model. Our findings show the two SARIMAX models to be most accurate. These models outperformed the forecasts published on the Entso-E platform in four out of the five Norwegian bidding zones, measured in MAPE and RMSE. Finally, we have shown that forecasting load with and without price information did not result in significant differences in accuracy. Our findings did not indicate an increase in difficulty of forecasting 2021 compared to 2019, neither for the three southern bidding zones with higher price increase nor the northern two zones.I denne masteroppgaven har vi forsøkt å finne den beste metoden for kortsiktig prognostisering av elektrisitets-etterspørsel i perioder med ekstreme prisbevegelser. Vi har besvart tre spørsmål knyttet til denne problemstillingen. Det første var hvilken modell som oppnår høyest nøyaktighet. Det andre var om våre modeller presterer bedre enn de publiserte prognosene på Entso-Es offentlig tilgjengelige data-plattform. Det tredje spørsmålet var om de ekstreme prisbevegelsene hadde noen effekt på nøyaktigheten av prognosene. Vi har laget to SARIMAX modeller, en Gradient boosting decision tree-, en Random Forest og en Multilayer perceptron-modell. Gjennom arbeidet har vi vist at de to SARIMAX-modellene presterer best. Disse modellene er mer nøyaktig enn prognosene publisert på Entso-Es plattform for fire av de fem norske strømregionene, målt i MAPE og RMSE. Til slutt har vi vist at prognoser gjort både med og uten prisinformasjon ikke gir signifikante forskjeller i nøyaktighet. Det ble heller ikke påvist en klar forskjell i vanskelighetsgraden av å prognostisere 2021 sammenlignet med 2019, verken for de sørlige prissonene med høy prisvekst eller de nordlige sonene med en lavere prisvekst.M-Ø

A SARIMAX coupled modelling applied to individual load curves intraday forecasting

A dynamic coupled modelling is investigated to take temperature into account in the individual energy consumption forecasting. The objective is both to avoid the inherent complexity of exhaustive SARIMAX models and to take advantage of the usual linear relation between energy consumption and temperature for thermosensitive customers. We first recall some issues related to individual load curves forecasting. Then, we propose and study the properties of a dynamic coupled modelling taking temperature into account as an exogenous contribution and its application to the intraday prediction of energy consumption. Finally, these theoretical results are illustrated on a real individual load curve. The authors discuss the relevance of such an approach and anticipate that it could form a substantial alternative to the commonly used methods for energy consumption forecasting of individual customers.Comment: 17 pages, 18 figures, 2 table

Forecasting of Frequency Containment Reserve Prices Using Econometric and Artificial Intelligence Approaches

The forecasting of control reserve prices is essential in order to participate reasonably in the auctions. Having identified a lack of related literature, we therefore deploy approaches based on auto-regressive and exogenous factors coming from econometrics and artificial intelligence and set up a forecasting framework. We use SARIMA and SARIMAX models as well as neural networks and forecast based on a rolling one-step forecast with re-estimation of the models. It turns out, that the combination of auto-regressive and exogenous factors yields the best results compared to approaches solely considering auto-regressive or exogenous factors. Further, the artificial intelligence approach outperforms the econometric approach in terms of forecast quality, whereas for the further use of the generated models, the econometric approach has advantages in terms of interpretability

Energy consumption forecasting using machine learning

Forecasting electricity demand and consumption accurately is critical to the optimal and costeffective operation system, providing a competitive advantage to companies. In working with seasonal data and external variables, the traditional time-series forecasting methods cannot be applied to electricity consumption data. In energy planning for a generating company, accurate power forecasting for the electrical consumption prediction, as a technique, to understand and predict the market electricity demand is of paramount importance. Their power production can be adjusted accordingly in a deregulated market. As data type is seasonal, Persistence Models (Naïve Models), Seasonal AutoRegressive Integrated Moving Averages with eXogenous regressors (SARIMAX), and Univariate Long-Short Term Memory Neural Network (LSTM) is used to explicitly deal with seasonality as a class of time-series forecasting models. The main purpose of this project is to perform exploratory data analysis of the Spain power, then use different forecasting models to once-daily predict the next 24 hours of energy demand and daily peak demand. To split the electricity consumption data from 2015 to 2018 into training and test sets, the first three years from 2015 and 2017 were used as the training set, while values from 2018 were used as the test set. The obtained results showed that the machine learning algorithms proposed in the recent literature outperformed the tested algorithms. Models are evaluated using root mean squared error (RMSE) to be directly comparable to energy readings in the data. RMSE has calculated two ways. First to represent the error of predicting each hour at a time (i.e. one error per-hourly slice). Second to represent the models’ overall performance. The results show that electricity demand can be modeled using machine learning algorithms, deploying renewable energy, planning for high/low load days, and reducing wastage from polluting on reserve standby generation, detecting abnormalities in consumption trends, and quantifying energy and cost-saving measures

A two-stage stochastic optimisation methodology for the operation of a chlor-alkali electrolyser under variable DAM and FCR market prices

The increased penetration of renewable energy sources in the electrical grid raises the need for more power system flexibility. One of the high potential groups to provide such flexibility is the industry. Incentives to do so are provided by variable pricing and remuneration of supplied ancillary services. The operational flexibility of a chlor-alkali electrolysis process shows opportunities in the current energy and ancillary services markets. A co-optimisation of operating the chlor-alkali process under an hourly variable priced electricity sourcing strategy and the delivery of Frequency Containment Reserve (FCR) is the core of this work. A short term price prediction for the Day-Ahead Market (DAM) and FCR market as input for a deterministic optimisation shows good results under standard DAM price patterns, but leaves room for improvement in case of price fluctuations, e.g., as caused by Renewable Energy Sources (RES). A two-stage stochastic optimisation is considered to cope with the uncertainties introduced by the exogenous parameters. An improvement of the stochastic solution over the deterministic Expected Value (EV) solution is shown