Local Short Term Electricity Load Forecasting: Automatic Approaches

Short-Term Load Forecasting (STLF) is a fundamental component in the efficient management of power systems, which has been studied intensively over the past 50 years. The emerging development of smart grid technologies is posing new challenges as well as opportunities to STLF. Load data, collected at higher geographical granularity and frequency through thousands of smart meters, allows us to build a more accurate local load forecasting model, which is essential for local optimization of power load through demand side management. With this paper, we show how several existing approaches for STLF are not applicable on local load forecasting, either because of long training time, unstable optimization process, or sensitivity to hyper-parameters. Accordingly, we select five models suitable for local STFL, which can be trained on different time-series with limited intervention from the user. The experiment, which consists of 40 time-series collected at different locations and aggregation levels, revealed that yearly pattern and temperature information are only useful for high aggregation level STLF. On local STLF task, the modified version of double seasonal Holt-Winter proposed in this paper performs relatively well with only 3 months of training data, compared to more complex methods

Moving holidays' effects on the Malaysian peak daily load

Malaysia’s yearly steady growth in electricity consumption as a result of fast development in various sectors of the Malaysian economy have increased the need to have a more robust, reliable and accurate load forecasting for short -, medium-, or long-term. A reliable method for short term load forecasting is crucial to any decision maker in a power utility company. Many studies have been made to improve the forecasting accuracy using various methods. The forecasting errors for the holiday seasons are known to be higher than those for weekends. This paper aims to determine which model would be a better model to estimate the holiday effects and therefore give a better forecasting accuracy for the peak daily load in Malaysia. Some of the holiday effects in Malaysia are from Eid ul-Fitr, Christmas, Independence Day and Chinese New Year. The seasonal ARIMA (SARIMA) and Dynamic Regression (DR) or Transfer function modelling are considered. Furthermore, the final selection of the models depends on the Mean Absolute Percentage Error (MAPE) and others such as the sample autocorrelation function (ACF), the sample partial autocorrelation function (PACF) and a bias-corrected version of the Akaike’s information criterion (AICC) statistic. The Dynamic Regression (DR) model recorded 2.22% as the lowest MAPE value for the 2004 New Year’s Eve and 2.39% for the seven days ahead forecasting. And therefore, DR model is the most appropriate model to be considered for forecasting any public holidays in Malaysia

Determinants of power spreads in electricity futures markets: A multinational analysis. ESRI WP580, December 2017

The growth in variable renewable energy (vRES) and the need for flexibility in power systems go hand in hand. We study how vRES and other factors, namely the price of substitute fuels, power price volatility, structural breaks, and seasonality impact the hedgeable power spreads (profit margins) of the main dispatchable flexibility providers in the current power systems - gas and coal power plants. We particularly focus on power spreads that are hedgeable in futures markets in three European electricity markets (Germany, UK, Nordic) over the time period 2009-2016. We find that market participants who use power spreads need to pay attention to the fundamental supply and demand changes in the underlying markets (electricity, CO2, and coal/gas). Specifically, we show that the total vRES capacity installed during 2009-2016 is associated with a drop of 3-22% in hedgeable profit margins of coal and especially gas power generators. While this shows that the expansion of vRES has a significant negative effect on the hedgeable profitability of dispatchable, flexible power generators, it also suggests that the overall decline in power spreads is further driven by the price dynamics in the CO2 and fuel markets during the sample period. We also find significant persistence (and asymmetric effects) in the power spreads volatility using a univariate TGARCH model

Forecasting from one day to one week ahead for the Spanish system operator

This paper discusses the building process and models used by Red Eléctrica de España (REE), the Spanish system operator, in short-term electricity load forecasting. REE's forecasting system consists of one daily model and 24 hourly models with a common structure. There are two types of forecasts of special interest to REE, several days ahead predictions for daily data and one day ahead hourly forecasts. Accordingly, forecast accuracy is assessed in terms of their errors. For doing so we analyze historical, real time forecasting errors for daily and hourly data for the year 2006, and report forecasting performance by day of the week, time of the year and type of day. Other aspects of the prediction problem, like the influence of the errors in predicting temperature on forecasting the load several days ahead, or the need for an adequate treatment of special days, are also investigated

Cross-city hedging with weather derivatives using bivariate DCC GARCH models

As monopolies gave their way to competitive wholesale electricity markets, volumetric risk came into play. Electricity supplier can buy weather derivatives to protect from volumetric risk due to unexpected weather conditions. However, contracts can only be negotiated for weather variables measured at few selected locations. To hedge their specific risk, electricity supplier have to correlate their risk with the risk at tradeable locations. In this paper, we concentrate on temperature derivatives. More precisely, we examine if and how bivariate GARCH models with dynamic conditional correlations can help in modelling correlation between two distinct temperature time series. The knowledge of correlation dynamics between the temperature time series enables an electricity supplier to correlate his risk with the risk of a traded city and to construct a sensible hedge. It turns out that the application of bivariate DCC GARCH models to three German temperature time series provides encouraging results. --

Using high-frequency data and time series models to improve yield management

We show the potential contribution of time series models (TSM) to the analysis of high frequency (less than monthly) time series of economic activity. The evolution of the series is induced by stable patterns of behavior of economic agents; but these patterns are so complex that simple smoothing techniques or subjective forecasting can not consider all underlying factors and TSM are needed if a full efficient analysis is to be carried out. The main ideas are illustrated with an apllication to Spanish daily electricity consumption