A major part of renewable electricity production is characterized by a large degree of intermittency driven by the natural variability of climate factors such as air temperature, wind velocity, solar radiation, precipitation, evaporation, and river runoff The main strategies to handle this intermittency include energy-storage, -transport, -diversity and -information The three first strategies smooth out the variability of production in time and space, whereas the last one aims a better balance between production and demand This study presents a literature review on the space-time variability of climate variables driving the intermittency of wind-, solar- and hydropower productions and their joint management in electricity systems A vast body of studies pertains to this question bringing results covering the full spectrum of resolutions and extents, using a variety of data sources, but mostly dealing with a single source Our synthesis highlights the consistency of these works, and, besides astronomic forcing, we identify three broad climatic regimes governing the variability of renewable production and load At sub-daily time scales, the three considered renewables have drastically different pattern sizes in response to small scale atmospheric processes At regional scales, large perturbation weather patterns consistently control wind and solar production, hydropower having a clearly distinct type of pattern At continental scales, all renewable sources and load seem to display patterns of constant space characteristics and no indication of marked temporal trends

https://hal.archives-ouvertes.fr/hal-01555809/document

Space-time variability of climate variables and intermittent renewable electricity production – A review

Potential gains from expanding regional electricity trade in South Asia

With the globally increasing electricity demand, its related uncertainties are on the rise as well. Therefore, a deeper insight of load forecasting techniques for projecting future electricity demands becomes imperative for business entities and policy makers. The electricity demand is governed by a set of different variables or “electricity demand determinants”. These demand determinants depend on forecasting horizons (long term, medium term, and short term), the load aggregation level, climate, and socio-economic activities. In this paper, a review of different electricity demand forecasting methodologies is provided in the context of a group of low and middle income countries. The article presents a comprehensive literature review by tabulating the different demand determinants used in different countries and forecasting the trends and techniques used in these countries. A comparative review of these forecasting methodologies over different time horizons reveals that the time series modeling approach has been extensively used while forecasting for long and medium terms. For short term forecasts, artificial intelligence-based techniques remain prevalent in the literature. Furthermore, a comparative analysis of the demand determinants in these countries indicates a frequent use of determinants like the population, GDP, weather, and load data over different time horizons. Following the analysis, potential research gaps are identified, and recommendations are provided, accordingly.

https://www.mdpi.com/2071-1050/12/15/5931/pdf

A Review of Electricity Demand Forecasting in Low and Middle Income Countries: The Demand Determinants and Horizons

Electricity, a fundamental commodity, must be generated as per required utilization which cannot be stored at large scales. The production cost heavily depends upon the source such as hydroelectric power plants, petroleum products, nuclear and wind energy. Besides overproduction and underproduction, electricity demand is driven by metrological parameters, economic and industrial activities. Therefore, the region specific accurate electric load forecasting can help to effectively manage, plan, and schedule appropriate low cost electricity generation units to decrease per unit cost and provision of on time energy for maximum financial benefits. Machine learning (ML) offers different supervised learning algorithms including multiple linear regression, support vector regressors with different kernels, k-nearest neighbors, Random Forest and AdaBoost to forecast the time series data, but the performance of these algorithms is data dependent. It is vitally important to consider correlated metrological parameters of the specific region for accurate prediction of electricity load demand using ML based forecasting models to minimize the price per unit. In this study, an algorithm is proposed to select least cost electric load forecasting model (lcELFM) using correlated meteorological parameters. We developed least cost forecasting models by minimizing root mean squared error, mean absolute error, and mean absolute percentage error. For simulations, the recorded electricity demand data is taken from a substation of water and power development authority Muzaffarabad city from $1^{\mathrm {st}}$ January 2014 to $31^{\mathrm {st}}$ December 2015. The meteorological time series data are obtained from the substation of Pakistan meteorological department for the same period and same region. Empirical results demonstrate the robustness of the proposed algorithm to select lcELFM. Moreover, SVR (Radial) based electric load forecasting model proves to be the robust model when built using correlated features (temperature and dew point) for the said region and in turn can save up to PKR 0.313 million daily.

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Machine Learning Based Cost Effective Electricity Load Forecasting Model Using Correlated Meteorological Parameters

The increasing shortage of electricity in Pakistan disturbs almost all sectors of its economy. As, for accurate policy formulation, precise and efficient forecasts of electricity consumption are vital, this paper implements a forecasting procedure based on components estimation technique to forecast medium-term electricity consumption. To this end, the electricity consumption series is divided into two major components: deterministic and stochastic. For the estimation of deterministic component, we use parametric and nonparametric models. The stochastic component is modeled by using four different univariate time series models including parametric AutoRegressive (AR), nonparametric AutoRegressive (NPAR), Smooth Transition AutoRegressive (STAR), and Autoregressive Moving Average (ARMA) models. The proposed methodology was applied to Pakistan electricity consumption data ranging from January 1990 to December 2015. To assess one month ahead post-sample forecasting accuracy, three standard error measures, namely Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE), were calculated. The results show that the proposed component-based estimation procedure is very effective at predicting electricity consumption. Moreover, ARMA models outperform the other models, while NPAR model is competitive. Finally, our forecasting results are comparatively batter then those cited in other works.

Modeling and Forecasting Medium-Term Electricity Consumption Using Component Estimation Technique

Nowadays, different sectors of the economy are being significantly affected by the weather vagaries. Electricity market is one of the most sensitive sectors, due to the fact that electricity demand is connected to the numerous climatic variables, especially the atmospheric temperature. In this paper we have deduced the link between electricity consumption and mean monthly maximum temperature index in Pakistan, as a case study. ARIMA time series forecast model is developed for the temperature index. The forecast values of mean monthly maximum temperature shows an increasing trend. Linear trend model for electricity consumption is also developed as a function of temperature. Electricity consumption reveals a significant trend due to increase in temperature and socio- economic factors. The monthly behavior of our forecast values depicts that the electricity consumption is more for summer season, and this demand will be highest (6785.6 GWh) in July 2020, due to rise in temperature. Forecast model reveals that the electricity consumption (EC) and mean monthly maximum temperature are increasing with the passage of time.

/pdf/relationship-between-extreme-temperature-and-electricity-2ss7xv7gmb.pdf

Muhammad Akram Sharif

Papers

Relationship between extreme temperature and electricity demand in pakistan